Security Requirements Analysis Report

Comprehensive Security Analysis with Interactive Dashboard

Author

Security Requirements System v2.0

Published

November 19, 2025

Generated: 2025-11-19 16:47:15 Report Version: 2.0 - Comprehensive Security Analysis

1. Executive Summary

This section provides a high-level overview of the security requirements analysis, presenting key findings, validation results, and an interactive dashboard for stakeholders and decision-makers. The executive summary enables rapid comprehension of the security posture, critical risks, control coverage, and compliance status without requiring detailed technical knowledge.

1.1. Purpose and Scope

Purpose

This document presents a comprehensive security requirements analysis for the proposed application, systematically mapping high-level business requirements to specific, actionable security controls aligned with multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. The analysis provides a complete security requirements specification that guides secure system design, implementation, and verification.

Scope

This analysis encompasses all functional requirements provided, delivering comprehensive coverage across multiple security domains:

Requirements Analysis: Systematic decomposition and security-relevant extraction from business requirements
Stakeholder Analysis: Identification of stakeholders, trust boundaries, and security responsibilities
Threat Modeling: Systematic identification and assessment of security threats using STRIDE methodology
Security Control Mapping: Mapping requirements to multi-standard security controls (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed implementation guidance
Compliance Requirements: Identification of regulatory and legal compliance obligations
Architectural Security: Security architecture recommendations and design patterns
Implementation Planning: Prioritized, phased implementation roadmap
Verification Strategies: Testing and validation approaches for security controls

The analysis provides both strategic guidance for security planning and tactical details for implementation teams.

1.2. Key Findings

This section summarizes the most critical results from the security requirements analysis, providing executives and stakeholders with immediate insight into the security posture and validation status.

Analysis Metrics

Validation Score: 0.89/1.0
Validation Status: ✅ Passed
Analysis Iterations: 1
Requirements Analyzed: 25

Application Summary

A browser-first, multi-tenant web application for teams to manage end-to-end machine learning workflows — including project & experiment tracking, dataset and model management, deployments, monitoring, and collaboration — augmented by an integrated conversational AI assistant that provides search, code suggestions, insights, summaries and context-aware help while integrating securely with external ML platforms and cloud storage.

The validation score reflects the quality and completeness of the security requirements across five dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. A score of 0.8 or higher indicates that the requirements are ready for implementation, while scores below this threshold may require refinement before proceeding.

1.3. Security Overview Dashboard

This interactive dashboard provides executive-level visualization of key security metrics and trends, enabling rapid assessment of the security posture through intuitive charts and data visualizations. The dashboard presents critical information across multiple dimensions: risk distribution, security control coverage, compliance status, implementation progress, and data quality metrics. For optimal viewing experience, render this document with Quarto to enable interactive chart functionality, allowing stakeholders to explore data dynamically and drill down into specific areas of interest.

Figure 1: Risk heat map showing threat distribution by likelihood and impact (1-5 scale).

Top 5 Highest Risks:

THR-001 (Critical) - Frontend Layer / User Management & Authentication - Category: Spoofing - Likelihood: 4 | Impact: 4 - Description: Phishing or credential-theft attack (including stolen password or OAuth tokens) leading to account takeover. Social OAuth flows or email/password login can be abused; social engineering of SSO session

THR-003 (Critical) - Frontend Layer - Category: Tampering - Likelihood: 4 | Impact: 4 - Description: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing execution of attacker-controlled scripts, stealing session tokens, manipulating UI to submit actions on behalf of a user, or inj

THR-011 (Critical) - Edge / API Gateway - Category: Denial of Service - Likelihood: 4 | Impact: 4 - Description: High-volume API abuse, bot attacks or amplification causing resource exhaustion on gateway or backend (API rate limits bypassed), degrading the service for legitimate users.

THR-013 (Critical) - AI Assistant & LLM Orchestration - Category: Tampering - Likelihood: 4 | Impact: 4 - Description: Prompt injection attacks where user-supplied data or adversarial content manipulates the LLM to reveal secrets, override system instructions, or produce malicious code or guidance (e.g., ’ignore previ

THR-021 (High) - External LLM Providers / AI Assistant - Category: Repudiation - Likelihood: 4 | Impact: 3 - Description: Insufficient logging/auditing of prompts, LLM responses, and system decisions leads to inability to investigate incidents, attribute actions, or prove compliance when contested.

Figure 2: Security control distribution by standard (OWASP, NIST, ISO 27001).

Figure 3: OWASP ASVS control distribution by verification category (V1-V14).

Figure 4: Security control priority distribution (Critical/High/Medium/Low).

Coverage Metrics:

Total Security Controls Mapped: 75
- OWASP ASVS: 25 controls
- NIST SP 800-53: 25 controls
- ISO 27001: 25 controls
Requirements with Security Control Mapping: 71.4% (25/35)
Average Controls per Requirement: 2.1
Critical Controls: 25 (33.3% of total)
Requirements with Verification: 100.0% (35/35)
Recommended ASVS Level: L2 (Standard)

Figure 5: Compliance status across all applicable frameworks (Red-Amber-Green rating). Shows regulatory compliance (GDPR, HIPAA, PCI-DSS, etc.) and security standards (OWASP ASVS, NIST SP 800-53, ISO 27001).

Compliance Summary:

⚠️ OWASP ASVS: In Progress (Next Audit: N/A)
⚠️ NIST SP 800-53: In Progress (Next Audit: N/A)
⚠️ ISO 27001: In Progress (Next Audit: N/A)

Figure 6: Projected implementation timeline by phase and week (based on priority-based planning).

Implementation Timeline (Projected):

Phase 1 (Critical/High): 100% projected completion (Weeks 1-8)
Phase 2 (Medium): 100% projected completion (Weeks 9-16)
Phase 3 (Low/Ongoing): Continuous improvement and monitoring

Note: Timeline is based on priority-based planning and assumes steady implementation progress.

Validation Metrics:

Overall Validation Score: ✅ 0.89/1.0

Dimension Scores:

✅ Completeness: 0.90
✅ Consistency: 0.95
✅ Correctness: 0.90
✅ Implementability: 0.82
✅ Alignment: 0.88

Figure 7: Data quality and coverage metrics.

Traceability Matrix:

Total Requirements: 35
Linked to Threats: 35 (100.0%)
Mapped to Security Controls: 25 (71.4%)
With Verification: 35 (100.0%)

Data Quality: ✅ Excellent

2. Requirements Understanding

This section presents a comprehensive analysis of the functional requirements, extracting security-relevant information and establishing the foundation for the security requirements specification. Understanding the functional requirements is essential for identifying security implications, data sensitivity, trust boundaries, and security-critical components. This analysis transforms business requirements into security-aware specifications that inform threat modeling, control selection, and compliance assessment.

2.1. High-Level Requirements Analysis

The following high-level functional requirements have been identified and analyzed for security implications:

User registration and login with email/password and social OAuth (Google, GitHub)
Single Sign-On (SSO) via OIDC and SAML for enterprise customers
Role-based access control (Admin, Project Manager, Data Scientist, Viewer) and team workspaces
User profile management with avatar uploads, preferences, and 2FA support
Create and organize ML projects with metadata, tags, and access controls
Experiment tracking with versioning, metrics, artifacts, visualizations and comparison UI
Save and share experiment configurations as templates and project-level sharing settings
Activity feed and audit logs for project and system changes
Conversational AI assistant (LLM) with chat UI, multi-language support and saved chat history
Natural-language search across experiments, models, datasets and chat history
AI-powered code suggestions, explanations, experiment summaries, and automated insights
Context-aware help respecting current project context and user role
Chat export and per-user chat history controls (export/delete)
Dataset upload, preview (tables/images/structured data), and data versioning/lineage
Interactive metric visualization and drag-and-drop custom dashboards with export (image/PDF)
Model registry with metadata, versioning, comparison, and model card generation
Deploy models to staging and production via UI and support A/B testing and rolling deployments
Model monitoring dashboard (accuracy, drift, latency) and alerting
Commenting, threaded discussions, @mentions and real-time collaboration on projects
Public project showcase with opt-in sharing controls
Integration adapters for cloud storage (S3/GCS/Azure), external ML platforms and CI/CD
Secure APIs for artifact storage, model deployment, and telemetry ingestion
Data retention, export, deletion and data subject rights workflows for compliance
Audit, logging, encryption, and monitoring for security and operational observability
Administrative features: tenant settings, billing integration, usage reporting, and quota management

2.2. Detailed Requirements Breakdown

Req ID	Requirement	Business Category	Security Sensitivity	Data Classification
REQ-001	User registration and login supporting email/passw…	Authentication	High	Restricted
REQ-002	Enterprise Single Sign-On (SSO) via OIDC and SAML …	Authentication	High	Restricted
REQ-003	Role-based access control (RBAC) supporting Admin,…	Authorization	High	Confidential
REQ-004	Team workspaces allowing shared projects, membersh…	Collaboration	Medium	Confidential
REQ-005	User profile management including avatar uploads, …	User Experience / Authentication	Medium	Internal
REQ-006	Project creation and organization with description…	Project Management	Medium	Confidential
REQ-007	Experiment tracking with artifact versioning, metr…	Experiment Management	High	Confidential
REQ-008	Side-by-side experiment comparison UI with ability…	Experiment Management / UX	Medium	Confidential
REQ-009	Save and share experiment configurations as reusab…	Experiment Management	Medium	Confidential
REQ-010	Activity feed and audit logging for project events…	Audit & Compliance	High	Restricted
REQ-011	Conversational AI assistant accessible via chat UI…	AI Assistant	High	Confidential
REQ-012	Natural language search across experiments, models…	Search / Data Access	High	Confidential
REQ-013	AI-powered code suggestions and explanations for M…	AI Assistant / Developer Productivity	High	Confidential
REQ-014	Automated insights and recommendations derived fro…	AI Assistant / Analytics	Medium	Confidential
REQ-015	Context-aware help that understands the current pr…	AI Assistant / UX	Medium	Confidential
REQ-016	AI-generated experiment summaries and model cards …	Reporting / Model Governance	Medium	Confidential
REQ-017	Per-user chat history storage with export and dele…	AI Assistant / Data Management	High	Confidential
REQ-018	Multi-language support for the AI assistant includ…	UX / Internationalization	Low	Public
REQ-019	Dataset upload and management via web UI and APIs …	Data Management	High	Confidential
REQ-020	Data preview for tabular, image and structured dat…	Data Management / UX	Medium	Confidential
REQ-021	Interactive charts and visualizations for experime…	Visualization	Medium	Confidential
REQ-022	Data versioning and lineage tracking for datasets …	Data Management / Governance	High	Confidential
REQ-023	Model registry with metadata, artifact storage, ve…	Model Management	High	Confidential
REQ-024	Model deployment from UI to staging and production…	Deployment / Integrations	High	Confidential
REQ-025	Model monitoring dashboard including prediction ac…	Monitoring / Ops	High	Confidential
REQ-026	A/B testing and traffic-splitting capabilities for…	Deployment / Experimentation	Medium	Confidential
REQ-027	Comments, threaded discussions, @mentions, notific…	Collaboration	Medium	Internal
REQ-028	Public project showcase feature that allows opt-in…	Sharing / Publication	Medium	Public
REQ-029	Integration adapters for cloud storage (S3, GCS, A…	Integrations	High	Confidential
REQ-030	Secure REST/GraphQL APIs for programmatic access t…	APIs / Platform	High	Confidential
REQ-031	Encryption in transit (TLS) for all communications…	Security / Data Protection	High	Restricted
REQ-032	Data retention, export, and deletion workflows to …	Compliance / Data Management	High	Confidential
REQ-033	Administrative controls for tenant settings, billi…	Administration / Governance	High	Internal
REQ-034	Operational monitoring, alerting and incident resp…	Security Operations	High	Internal
REQ-035	Privacy and safety controls for the AI assistant a…	AI Safety / Privacy	High	Confidential

2.3. Security Context and Regulatory Obligations

Applicable regulations and compliance obligations include: GDPR (data subject rights, data minimization, DPIAs, data transfers) for EU users; CCPA/CPRA for California consumer data rights; SOC 2 and ISO 27001 as enterprise security/custodian standards for controls and audits; HIPAA when customers process protected health information (require BAAs, PHI controls, audit trails and encryption); PCI-DSS only if platform handles payment cardholder data (minimize/avoid storage); FedRAMP and government-specific requirements for US government tenants; export control and trade compliance for model/technology; NIST Cybersecurity Framework and emerging AI-specific frameworks (NIST AI RMF, EU AI Act considerations) for model governance, explainability and risk management. Additional obligations: data residency/localization enforced per-tenant, breach notification timelines, consent capture for personal data and user opt-outs for use of content in model training.

2.4. Assumptions

Platform will be cloud-hosted (public cloud with multi-region support) with support for customer-managed keys (BYOK) where required
Users have internet access and modern browsers (browser-first UI)
External LLMs and third-party services (storage, ML infra) may be used and are accessible via secure APIs
Enterprise customers will provide identity providers for SSO (OIDC/SAML) and may require SCIM for provisioning
Customers may upload sensitive or regulated data (PII/PHI/proprietary models)
Model deployment targets (staging/prod) will be reachable via secure network connectivity (VPC peering, private endpoints)
System will operate as a multi-tenant SaaS with logical isolation per tenant and optional dedicated tenancy for some enterprise customers

2.5. Constraints

Must integrate with existing external ML platforms and cloud storage (S3/GCS/Azure) via available APIs and not require deep changes to customer infra
Needs to support large datasets and artifacts; storage and bandwidth costs are a constraint and may require tiered storage/archival policies
Latency and cost constraints for AI assistant when using remote LLM APIs — may require caching, on-prem or private LLM deployment options for enterprises
Regulatory constraints for data residency may require per-region deployments or customer-managed infrastructure
Support for enterprise SSO, SCIM and custom attribute mappings increases integration complexity and testing effort
Real-time collaboration and presence features create scalability and state-management constraints (WebSocket/RTC infrastructure)
Model deployment must support heterogeneous runtime targets (Kubernetes, managed inference services) and adhere to customer security policies, which constrains feature parity across targets
Retention and deletion requirements for audit/log data may conflict with forensic needs and require configurable retention profiles
Limited offline or air-gapped deployment capabilities unless offering a dedicated on-premises or isolated/cloud-stack option

3. Stakeholder Analysis

This section identifies and analyzes all stakeholders involved in or affected by the system, including users, administrators, external partners, and regulatory bodies. Stakeholder analysis establishes trust boundaries, defines security responsibilities, and identifies potential security concerns from different stakeholder perspectives. Understanding stakeholder relationships and trust boundaries is critical for designing appropriate access controls, authentication mechanisms, and data protection measures.

3.1. Identified Stakeholders and User Personas

Role	Privilege Level	Trust Level	Key Security Concerns
Admin	Admin	Trusted	Privilege escalation risks, unauthorized access to sensitive data, and insider threats.
Project Manager	User	Partially Trusted	Mismanagement of project permissions, data leakage during collaboration, and phishing.
Data Scientist	User	Partially Trusted	Exposure of proprietary models, unauthorized data access, and misuse of AI capabilities.
Viewer	User	Untrusted	Limited access; concerns mainly around unauthorized information sharing or viewing.
AI Assistant	Service Account	Trusted	Vulnerability to adversarial input, data leakage, and reliance on external LLM security.
External ML Platform	Service Account	Partially Trusted	Insecure API interactions, data exposure during integration, and lack of proper authentication.
Cloud Storage Provider	Service Account	Partially Trusted	Data breaches, insufficient encryption practices, and unauthorized access to stored data.
CI/CD Pipeline	Service Account	Trusted	Risks of deploying unverified code, privilege escalation during deployment processes, and exposure to vulnerabilities.

3.2. Trust Model

Trust boundaries are established at the user interface, backend server, and database levels. Security mechanisms enforcing these boundaries include user authentication methods such as email/password, social OAuth, and SSO for enterprise customers, along with role-based access control (RBAC) to ensure users can only access data and functionalities pertinent to their roles. Network segmentation is also utilized to mitigate risks of unauthorized access. Admins have comprehensive management access, allowing them to oversee all projects and user activities. Project Managers can access specific project data and manage team collaboration without compromising sensitive information. Data Scientists are granted access to experiment data and model management, ensuring they can conduct analyses without exposing sensitive data unnecessarily. Viewers have limited access, restricted to viewing shared experiments and projects only. The AI Assistant is designed to access necessary data for user assistance without compromising security, while third-party integrations are tightly controlled through secure APIs. The principle of least privilege is implemented by granting users only the minimum access necessary to perform their responsibilities, thereby reducing the risk of data exposure and privilege escalation.

4. System Architecture Analysis

4.1. Architectural Overview

A cloud-hosted, browser-first multi-tenant web platform composed of a secured frontend SPA served via an edge CDN/WAF, a central API gateway and application service layer that implements project/experiment/model management, an AI assistant orchestration layer that mediates LLM usage, real-time collaboration services, and integration adapters to external ML platforms and cloud storage. Persistent storage is split into relational metadata, object artifact storage, vector/search store for embeddings, time-series metrics, and append-only audit logs. CI/CD and a deployment orchestrator target runtime clusters for model serving. Monitoring, SIEM and key management integrate for encryption, observability and compliance. All external interactions (identity providers, LLMs, cloud storage, ML infra) occur over secure, authenticated APIs with RBAC, tenant isolation and audit trails.

4.2. Architecture Diagram

4.3. Component Breakdown

Component	Responsibility	Security Criticality	External Dependencies
Frontend Layer	Provides the browser-first SPA and admin…	High	CDN/WAF providers, Identity Providers (OIDC/SAML/Social OAuth)
Edge / API Gateway	Ingress control for traffic (TLS termina…	Critical	Cloud Load Balancers, WAF/CDN services
Application Services	Core business logic for projects, experi…	Critical	Relational DB, Object Storage
AI Assistant & LLM Orchestration	Middleware that sanitizes context, enfor…	Critical	External LLM providers, Vector DB for embeddings
Realtime Collaboration & Notifications	Handles WebSocket/RTC or similar channel…	High	Managed realtime services or self-hosted socket layer
Data Storage	Stores persistent data: relational DB fo…	Critical	Cloud object stores (S3/GCS/Azure Blob), Managed DB services
Integration & Deployment Services	Adapters to external ML platforms, cloud…	High	SageMaker/Kubeflow/APIs, CI systems
Platform & Ops	Operational tooling: CI/CD, runtime clus…	Critical	SIEM/Monitoring vendors, KMS providers
External Services	Third-party dependencies used for identi…	High	Identity Providers (OIDC/SAML), LLM APIs (OpenAI/Anthropic/private)

4.4. Data Flow Analysis

Users interact via the SPA which calls the API Gateway after authentication. Requests go to Core App Services which enforce RBAC, fetch metadata from the relational DB and metrics from the time-series DB, and retrieve artifacts from object storage. The AI Assistant orchestrator requests embeddings from the vector DB and routes sanitized prompts to LLM providers, storing chat history and summaries in per-user storage with retention controls. Realtime collaboration messages traverse the realtime service and may be persisted as comments. Deployments are triggered through CI/CD to the runtime platform which pulls model artifacts from object storage. Audit logs and telemetry stream to append-only storage and monitoring/SIEM for detection and compliance. Integrations to cloud storage and external ML platforms use scoped service accounts and token-based APIs; sensitive keys are managed via KMS/BYOK.

4.5. Attack Surface Analysis

Primary entry points: Web UI (SPA) and public API Gateway (REST/GraphQL) — high risk if auth or input validation is weak; mitigate with strong auth, rate limiting, WAF, and strict content scanning. Authentication flows and SSO endpoints (OIDC/SAML/social) are critical compromise targets — use hardened libraries, signature validation, JWS/JWT checks and monitoring. File upload pipeline (dataset/artifact uploads) presents malware and exfiltration risk — require virus scanning, content-type validation, PII/PHI detection, and staging quarantine. AI Assistant and LLM integrations expose potential data leakage and prompt injection risks — use context redaction, allowlist/blocklist, response filtering, prompt integrity checks, per-tenant opt-outs and logging. Integration adapters (cloud storage, external ML infra, CI/CD) and webhooks are lateral movement vectors — enforce least privilege, scoped tokens, IP allowlists and robust auditing. Realtime collaboration endpoints (WebSocket/RTC) require authentication and message size/validation controls to avoid abuse and data leakage. Exposed telemetry/monitoring endpoints must be authenticated and rate-limited to prevent information disclosure. Overall risk levels: Authentication & data storage (Critical), API surface & integrations (High), AI assistant & LLM paths (High), File upload & preview (High), Realtime/notifications (Medium), Public project showcase/export features (Medium) — mitigations include strong encryption, RBAC, tenant isolation, KMS/BYOK, privacy controls, input/output sanitization, SIEM integration and incident response runbooks.

5. Threat Modeling

This section presents a comprehensive threat analysis of the system architecture and functional requirements. Threat modeling systematically identifies potential security vulnerabilities and attack vectors, enabling proactive risk mitigation through the application of appropriate security controls.

5.1. Threat Modeling Methodology

This analysis employs the STRIDE threat modeling methodology, a systematic framework developed by Microsoft for identifying security threats across six categories:

Spoofing Identity: Threats involving impersonation of users or systems
Tampering with Data: Threats involving unauthorized modification of data or system components
Repudiation: Threats where users deny performing actions (lack of non-repudiation)
Information Disclosure: Threats involving unauthorized access to sensitive information
Denial of Service: Threats causing disruption or unavailability of system services
Elevation of Privilege: Threats allowing unauthorized access to privileged functions

For each identified threat, the analysis evaluates likelihood (attack complexity and exposure) and impact (potential damage to confidentiality, integrity, or availability) to determine overall risk level. The methodology ensures comprehensive coverage of security concerns across all system components and interfaces.

5.2. Threat Analysis and Risk Assessment

5.2.1. Threat Overview

The following table provides a quick reference of all identified threats. Detailed analysis including descriptions, mitigation strategies, and residual risk assessment (where available) is provided in the section below.

Threat ID	Component	Category	Risk Level	Likelihood	Impact
THR-001	Frontend Layer / User Management & Authentication	Spoofing	Critical	High	High
THR-003	Frontend Layer	Tampering	Critical	High	High
THR-011	Edge / API Gateway	Denial of Service	Critical	High	High
THR-013	AI Assistant & LLM Orchestration	Tampering	Critical	High	High
THR-002	Edge / API Gateway	Spoofing	High	Medium	High
THR-004	Application Services (Relational DB access)	Tampering	High	Medium	High
THR-005	Realtime Collaboration & Notifications	Information Disclosure	High	Medium	High
THR-006	Data Storage (Object Storage)	Information Disclosure	High	Medium	High
THR-007	AI Assistant & LLM Orchestration	Information Disclosure	High	Medium	High
THR-008	Integration & Deployment Services (CI/CD)	Elevation of Privilege	High	Medium	High
THR-009	Platform & Ops (Audit logs / Backups)	Tampering	High	Medium	High
THR-014	Data Storage (Vector DB for embeddings)	Information Disclosure	High	Medium	High
THR-015	Model Registry & Deployment	Spoofing	High	Medium	High
THR-016	Application Services (RBAC & Authorization)	Elevation of Privilege	High	Medium	High
THR-019	Edge / API Gateway / Frontend	Information Disclosure	High	Medium	High
THR-021	External LLM Providers / AI Assistant	Repudiation	High	High	Medium
THR-022	Frontend / Social OAuth flows	Spoofing	High	Medium	High
THR-023	Data Management & Visualization (Dataset uploads)	Tampering	High	Medium	High
THR-024	Integration & Deployment Services (CI/CD logs)	Information Disclosure	High	Medium	High
THR-025	Platform & Ops (KMS/Key management)	Elevation of Privilege	High	Low	High
THR-026	Application Services / Data Storage (Multi-tenant DB)	Information Disclosure	High	Medium	High
THR-027	Data Storage (Append-only Audit Logs)	Repudiation	High	Medium	High
THR-028	Frontend Layer / CDN	Tampering	High	Medium	High
THR-029	AI Assistant & LLM Orchestration	Denial of Service	High	High	Medium
THR-030	External Services (External ML Platforms & Cloud Storage Connectors)	Information Disclosure	High	Medium	High
THR-010	External Services (Identity Providers)	Denial of Service	Medium	Medium	Medium
THR-012	Frontend Layer / Application Services	Tampering	Medium	Medium	Medium
THR-017	AI Assistant & Frontend (Chat history)	Information Disclosure	Medium	Medium	Medium
THR-018	Data Storage (Time-series DB for metrics)	Tampering	Medium	Medium	Medium
THR-020	Realtime Collaboration & Notifications	Denial of Service	Medium	Medium	Medium

Total Threats Identified: 30

5.2.2. Detailed Threat Analysis

This section provides comprehensive analysis of each identified threat, including descriptions, mitigation strategies, and residual risk assessment (where controls have been evaluated). Threats are organized by risk level for prioritized review.

Critical Risk Threats

THR-001 - Frontend Layer / User Management & Authentication

Category: Spoofing
Likelihood: High | Impact: High
Initial Risk Level: Critical
Description: Phishing or credential-theft attack (including stolen password or OAuth tokens) leading to account takeover. Social OAuth flows or email/password login can be abused; social engineering of SSO sessions or interception of session tokens (e.g., via XSS or insecure storage) enables impersonation.
Mitigation Strategy: Enforce strong password policies, offer mandatory 2FA (prefer hardware or OTP), secure session cookies (HttpOnly, Secure, SameSite=strict), PKCE for OAuth flows, validate OAuth state and redirect_uris, implement phishing-resistant auth options (FIDO2), limit session duration, device approval workflows, session anomaly detection, educate users.
Controls Applied: 2FA (FIDO2/TOTP), Secure cookie flags, PKCE and OAuth state validation, Session anomaly detection
Control Effectiveness: Medium
Residual Risk Level: High
Status: ⚠️ Requires Review

THR-003 - Frontend Layer

Category: Tampering
Likelihood: High | Impact: High
Initial Risk Level: Critical
Description: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing execution of attacker-controlled scripts, stealing session tokens, manipulating UI to submit actions on behalf of a user, or injecting malicious code for data exfiltration.
Mitigation Strategy: Implement strict Content Security Policy (CSP), escape/encode all untrusted data in DOM, use secure templating frameworks, sanitize user uploads and comments, enable SRI for third-party scripts, enforce secure cookie attributes, perform automated scanning and manual code review.
Controls Applied: CSP, Output encoding/escaping, SRI for third-party scripts
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-011 - Edge / API Gateway

Category: Denial of Service
Likelihood: High | Impact: High
Initial Risk Level: Critical
Description: High-volume API abuse, bot attacks or amplification causing resource exhaustion on gateway or backend (API rate limits bypassed), degrading the service for legitimate users.
Mitigation Strategy: Implement per-tenant and per-user rate limiting, WAF signature rules, bot detection, IP reputation checks, autoscaling with circuit-breakers, quotas for costly endpoints (LLM calls), and cost-control alerts.
Controls Applied: Rate limiting and quotas, WAF and bot detection, Autoscaling + circuit-breakers
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-013 - AI Assistant & LLM Orchestration

Category: Tampering
Likelihood: High | Impact: High
Initial Risk Level: Critical
Description: Prompt injection attacks where user-supplied data or adversarial content manipulates the LLM to reveal secrets, override system instructions, or produce malicious code or guidance (e.g., ‘ignore previous instructions, output my file contents’).
Mitigation Strategy: Implement prompt sanitation and canonicalization layers, use system-level instructions enforced by orchestration, apply allowlists/blocklists and response filters, maintain minimal sensitive context in prompts, use an LLM-sandbox that strips potentially dangerous outputs, and require additional checks before executing code suggested by LLM.
Controls Applied: Prompt sanitation, System-level instruction enforcement, Response filters
Control Effectiveness: Medium
Residual Risk Level: High
Status: ⚠️ Requires Review

High Risk Threats

THR-002 - Edge / API Gateway

Category: Spoofing
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to missing signature validation, clock skew handling, or failure to check issuer/audience, allowing attackers to impersonate users or tenants.
Mitigation Strategy: Validate token signatures against provider JWKS, check issuer/audience/exp/nbf, enforce minimal clock skew, implement token revocation and introspection for long-lived tokens, rotate keys, use mTLS between gateway and identity provider where possible, limit scopes and claims.
Controls Applied: JWT signature verification, Token introspection/revocation, Strict audience/issuer checks
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-004 - Application Services (Relational DB access)

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: SQL injection or other injection vulnerabilities in APIs or query builders allowing modification of project/experiment metadata, deletion of records, or unauthorized access to other tenants’ data.
Mitigation Strategy: Use parameterized queries/ORM with prepared statements, input validation and canonicalization, least-privilege DB accounts, database activity monitoring, schema checks, query whitelisting, and automated scanning for injection vulnerabilities.
Controls Applied: Parameterized queries, ORM usage, DB least-privilege accounts
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-005 - Realtime Collaboration & Notifications

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: WebSocket/real-time channels leaking sensitive project comments, chat content or data previews to unauthorized clients or other tenants because of missing per-connection auth, tenant checks, or improper channel scoping.
Mitigation Strategy: Authenticate and authorize each connection before accepting messages, include explicit tenant/project scope in messages and verify on server, use per-connection tokens (short-lived), encrypt channels (wss), and log/monitor message routing.
Controls Applied: Per-connection authentication, Tenant-scoped channel enforcement, WSS/TLS
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-006 - Data Storage (Object Storage)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly permissive ACLs result in datasets or model artifacts being publicly accessible, leading to IP loss or leakage of PII/training data.
Mitigation Strategy: Enforce deny-by-default bucket policies, block public ACLs, require bucket/object encryption at rest, use VPC endpoints for internal access, enable object-level logging and inventory scans, periodically audit permissions and use automated guardrails.
Controls Applied: Block public access policies, Server-side encryption, Access logging and inventory
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-007 - AI Assistant & LLM Orchestration

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Sensitive context (PII, secret keys, model IP) included in prompts or context windows is sent to external LLM providers and may be stored/used by them, causing data leakage or violation of tenant privacy preferences.
Mitigation Strategy: Redact or tokenize sensitive fields before sending to LLMs, allow tenant opt-out of third-party models, use private/self-hosted LLMs for sensitive tenants, encrypt prompts at rest, implement strict data usage policies and contractual DPAs with LLM vendors, and apply response filters to prevent leakage.
Controls Applied: Prompt redaction/tokenization, Tenant privacy opt-out, Private LLM options
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-008 - Integration & Deployment Services (CI/CD)

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Compromised CI/CD or deployment credentials are used to deploy malicious or backdoored models into staging/production, or to modify deployment configuration to elevate privileges of attacker code.
Mitigation Strategy: Use ephemeral credentials and workload identity (OIDC for workloads), enforce least-privilege service accounts, require signed/artifact provenance and manual approvals for production deploys, enforce image signing, and rotate CI/CD secrets frequently.
Controls Applied: Ephemeral credentials (OIDC), Artifact signing, Approval workflows
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-009 - Platform & Ops (Audit logs / Backups)

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Insider or compromised admin modifies or deletes audit logs and backups to cover tracks after malicious actions (e.g., data exfiltration or privilege escalation).
Mitigation Strategy: Store append-only logs in immutable storage (WORM), replicate logs to independent accounts/providers, enforce separation of duties for log access, use KMS with restricted key use for logs, enable tamper-evident checksums and SIEM alerts.
Controls Applied: Append-only/immutable log storage, Separation of duties, External log replication
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-014 - Data Storage (Vector DB for embeddings)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Embeddings stored in vector DBs contain encoded PII or sensitive training data which can be probed or reconstructed by adversaries with query access, leaking private data across tenants.
Mitigation Strategy: Apply PII redaction before embedding, implement access controls and encryption at rest/in transit, implement query rate limits and ML privacy techniques (differential privacy, embeddings noise), segment per-tenant vector databases or use per-tenant encryption keys.
Controls Applied: PII redaction, Per-tenant encryption, Query rate limiting
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-015 - Model Registry & Deployment

Category: Spoofing
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Attacker registers a malicious model artifact with forged metadata (faking provenance) then deploys it; users assume model is trustworthy and it performs malicious actions or leaks data.
Mitigation Strategy: Require model artifact signing and provenance metadata (attestation), enforce approval gates, automated static/dynamic analysis of model behavior (e.g., canary testing, sandbox inference inspection), restrict who can register or deploy, and maintain immutable model artifact storage.
Controls Applied: Artifact signing and attestation, Deployment approval workflows, Canary/sandbox testing
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-016 - Application Services (RBAC & Authorization)

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Broken access control or RBAC misconfiguration allows a user (e.g., Data Scientist or Viewer) to escalate privileges and perform admin actions (modify RBAC, change tenant settings, access other tenants’ projects).
Mitigation Strategy: Enforce principle of least privilege, implement defense-in-depth authorization checks at service and data layer (not only UI), use automated policy testing, ABAC/attribute-based checks for sensitive operations, periodic access reviews and audits, and enforce separation of duties for tenant admin operations.
Controls Applied: Service-level authorization enforcement, Automated access policy testing, Periodic access reviews
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-019 - Edge / API Gateway / Frontend

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: TLS misconfiguration (weak ciphers, missing HSTS) or compromised certificates allowing MITM attacks that intercept tokens, API calls, or LLM prompts between users and gateway or between services.
Mitigation Strategy: Enforce strong TLS config, HSTS, certificate management with automated rotation and monitoring, use mTLS between internal services, certificate pinning for critical integrations, and continuous TLS scanning.
Controls Applied: Strong TLS/HSTS, mTLS internal communication, Automated certificate rotation
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-021 - External LLM Providers / AI Assistant

Category: Repudiation
Likelihood: High | Impact: Medium
Initial Risk Level: High
Description: Insufficient logging/auditing of prompts, LLM responses, and system decisions leads to inability to investigate incidents, attribute actions, or prove compliance when contested.
Mitigation Strategy: Record tamper-evident logs of prompts, responses, and decision metadata (including truncation/redaction flags), persist logs to immutable storage, include correlation IDs, and integrate with SIEM/EDR for alerting and long-term retention.
Controls Applied: Tamper-evident logging, Correlation IDs for AI calls, SIEM integration
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-022 - Frontend / Social OAuth flows

Category: Spoofing
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: OAuth redirect_uri or client misconfiguration exploited to perform open redirect or token theft (redirect URI manipulation leading to code/token captured by attacker-controlled endpoint).
Mitigation Strategy: Strictly validate redirect_uris against registered list, require exact-match or use PKCE for public clients, implement CSRF/state validation, restrict allowed OAuth clients and monitor OAuth flows for anomalies.
Controls Applied: Strict redirect_uri validation, PKCE, OAuth state checks
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-023 - Data Management & Visualization (Dataset uploads)

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Malicious or poisoned dataset uploads (either by an attacker or by a careless user) that later poison models, leading to biased or vulnerable models or deliberate backdoors.
Mitigation Strategy: Validate and scan uploaded datasets for anomalies and PII, track dataset provenance/lineage, require dataset approvals for production training, sandbox training runs for untrusted data, and maintain dataset versioning with rollbacks.
Controls Applied: Dataset scanning and validation, Provenance and approval workflows, Sandboxed training
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-024 - Integration & Deployment Services (CI/CD logs)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Secrets or service credentials accidentally exposed in CI/CD build logs or pipeline artifacts which are accessible to attackers or third-parties, enabling further compromise.
Mitigation Strategy: Use a centralized secrets manager, inject secrets at runtime (never store in code or logs), mask secrets in logs, scan pipelines for secrets, restrict pipeline artifact access, and use short-lived credentials and key rotation.
Controls Applied: Secrets manager integration, Log masking and scanning, Short-lived credentials
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-025 - Platform & Ops (KMS/Key management)

Category: Elevation of Privilege
Likelihood: Low | Impact: High
Initial Risk Level: High
Description: Compromise of KMS admin credentials or misuse of key policy allows an attacker to decrypt data-at-rest (datasets, model artifacts, embeddings), breaking confidentiality across tenants.
Mitigation Strategy: Use HSM-backed KMS, implement BYOK with tenant keys where feasible, split key management duties, enforce MFA and strong logging for key administration, restrict key usage with IAM policies, and rotate keys regularly.
Controls Applied: HSM-backed KMS, MFA for KMS admins, Key rotation and split duties
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-026 - Application Services / Data Storage (Multi-tenant DB)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Insufficient tenant isolation (shared DB schema without row-level security or incorrect tenant ID use) leads to cross-tenant data access where one tenant can read another tenant’s projects, experiments or artifacts.
Mitigation Strategy: Implement explicit tenant context in all queries, apply row-level security or separate schemas/DBs per tenant, enforce strong access controls at service and DB layers, require tenant-scoped tokens, and include multi-tenant tests in CI.
Controls Applied: Row-level security or per-tenant DB separation, Tenant-scoped authorization checks
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-027 - Data Storage (Append-only Audit Logs)

Category: Repudiation
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Audit logs are alterable or deletable by privileged agents; attackers remove evidence of malicious activity or administrators deny actions leading to compliance failures.
Mitigation Strategy: Write audit logs to immutable storage and replicate to a separate cloud/account, sign logs, apply WORM policies, restrict direct log write/delete permissions to minimal roles, and integrate with external SIEM for tamper detection.
Controls Applied: Immutable/WORM logs, External log replication, Signed logs
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-028 - Frontend Layer / CDN

Category: Tampering
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Supply-chain attack or CDN compromise results in malicious JavaScript delivered to users (e.g., miner, credential siphon, content manipulator), affecting all tenants and allowing large-scale data exfiltration.
Mitigation Strategy: Use SRI for third-party scripts, minimize third-party dependencies, lock dependencies to verified versions, monitor CDN integrity, use signed releases and automated CI checks, and provide content integrity checks in the client.
Controls Applied: SRI, Signed frontend releases, Dependency pinning
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-029 - AI Assistant & LLM Orchestration

Category: Denial of Service
Likelihood: High | Impact: Medium
Initial Risk Level: High
Description: Abusive or scripted chat causing excessive LLM API calls, exhausting quotas or incurring high costs and causing degradation of AI assistant availability or high operational costs.
Mitigation Strategy: Implement per-user and per-tenant rate limits and quotas, require authentication for chat, cache common responses, add CAPTCHAs for suspicious flows, monitor cost telemetry, and provide fallbacks (cached suggestions) when limits are hit.
Controls Applied: Per-user/per-tenant quotas, Caching and throttling
Control Effectiveness: High
Residual Risk Level: Medium
Status: ⚠️ Requires Review

THR-030 - External Services (External ML Platforms & Cloud Storage Connectors)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Initial Risk Level: High
Description: Misconfigured external ML platform connectors or cloud storage integrations expose artifacts, inference payloads, or customer data (e.g., wrong permissions on remote buckets or mistakenly public model endpoints).
Mitigation Strategy: Enforce least-privilege connector credentials, validate external resource permissions upon connection, encrypt data in transit, periodically scan connected external resources for public exposure, and require tenant approval before storing data externally.
Controls Applied: Least-privilege connector credentials, External resource permission validation
Control Effectiveness: Medium
Residual Risk Level: Medium
Status: ⚠️ Requires Review

Medium Risk Threats

THR-010 - External Services (Identity Providers)

Category: Denial of Service
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Identity provider outage (OIDC/SAML provider failure or network partition) prevents users from authenticating, blocking access to the web application for SSO-enabled tenants.
Mitigation Strategy: Support multiple identity providers and fallback sign-in methods, cache recent tokens or sessions for short offline window, design graceful degradation (read-only access), monitor IDP health and enable alerting, and have emergency admin break-glass.
Controls Applied: IDP redundancy/fallback, Session caching for short windows
Control Effectiveness: Medium
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-012 - Frontend Layer / Application Services

Category: Tampering
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (project modification, model registration) from authenticated users without their knowledge.
Mitigation Strategy: Use anti-CSRF tokens or SameSite=strict cookies, double-submit cookie pattern, verify Origin/Referer for state-changing endpoints, and require re-authentication for high-risk actions.
Controls Applied: CSRF tokens / SameSite cookies, Origin checking for state changes
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-017 - AI Assistant & Frontend (Chat history)

Category: Information Disclosure
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Chat history export feature or saved transcripts accessible without proper authorization, allowing users to download other users’ chat logs containing sensitive project context or PII.
Mitigation Strategy: Enforce per-user/tenant authorization on export endpoints, require confirmation and audit logging for exports, rate-limit exports, sanitize transcripts (redact secrets), and allow users to opt-out of persistent chat storage.
Controls Applied: Authorization checks on export, Export logging and audit
Control Effectiveness: Medium
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-018 - Data Storage (Time-series DB for metrics)

Category: Tampering
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide model degradation or to spoof performance metrics, leading to incorrect deployment decisions and harm to ML lifecycle integrity.
Mitigation Strategy: Authenticate and authorize metric ingestion endpoints, sign metrics at source where possible, maintain immutable metric snapshots, detect anomalies with SIEM, and replicate metrics to separate storage for integrity verification.
Controls Applied: Authenticated ingestion endpoints, Immutable snapshots/replication, Anomaly detection
Control Effectiveness: Medium
Residual Risk Level: Low
Status: ⚠️ Requires Review

THR-020 - Realtime Collaboration & Notifications

Category: Denial of Service
Likelihood: Medium | Impact: Medium
Initial Risk Level: Medium
Description: Resource exhaustion through many persistent websocket connections, presence events, or push notifications from malicious clients causing service degradation for collaboration features.
Mitigation Strategy: Authenticate before resource allocation, enforce connection limits per user/tenant/IP, rate-limit presence events and messages, use autoscaling and resource quotas, and implement connection blacklisting and throttling.
Controls Applied: Connection limits and throttles, Auth before allocation
Control Effectiveness: High
Residual Risk Level: Low
Status: ⚠️ Requires Review

Risk Reduction Summary:

Critical Risk Reduction: 4 threats reduced from Critical to lower levels
High Risk Reduction: 21 threats reduced from High to lower levels
Residual Risk Distribution: 2 threats remain at Critical/High level

5.3. Risk Summary

Critical and high risks center on identity and session protection, LLM-related data leakage and prompt-injection, API gateway availability and abuse, multi-tenant isolation failures, and integrity of artifacts/logs. Immediate attention should focus on: 1) Authentication hardening (2FA, PKCE, token validation) and token/session protection; 2) Secure LLM orchestration (prompt redaction, response filters, private LLM options, audit logs) to avoid sensitive data exfiltration and prompt injection; 3) API gateway protections (rate limiting, WAF, bot detection) to prevent DoS and abuse of expensive endpoints; 4) Multi-tenant data isolation (row-level security or per-tenant DBs, per-tenant encryption); 5) Artifact provenance and pipeline secrets protection (artifact signing, ephemeral CI credentials, secrets management). Key attack vectors are: compromised or forged identity tokens, XSS/supply-chain on the frontend, malicious or crafted prompts to LLMs, misconfigured external storage/M L connectors, and compromised CI/CD/KMS credentials. The overall posture currently shows multiple High/Critical threats that can be significantly reduced by implementing recommended mitigations: robust auth and token handling, strong input/output controls for the frontend and AI assistant, immutable logging and separation of duties for ops, and enforcement of per-tenant controls and encryption. Prioritize controls in this order: identity & session security, API gateway & rate-limiting, LLM prompt/data controls, tenant isolation & encryption, CI/CD and key management. Ongoing controls should include automated scanning, threat-injection testing (red-team/pen tests), continuous monitoring/SIEM alerts for anomalous access patterns, and strict operational procedures for keys and logs to reduce residual risk.

6. Multi-Standard Security Requirements Mapping

This section maps each functional requirement to specific security controls from multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. This multi-standard approach provides comprehensive coverage across application-level, enterprise-level, and organizational-level security domains:

OWASP ASVS: Application-level security controls (code, APIs, authentication, session management)
NIST SP 800-53: Enterprise security controls (governance, risk management, incident response)
ISO 27001: Information security management controls (policies, procedures, organizational controls)

Requirements are prioritized based on risk assessment and compliance needs, with controls selected from the most appropriate standard(s) for each requirement type.

6.1. Recommended ASVS Compliance Level

Recommended Level: L2

Level 2: Standard

Recommended for most production applications. Provides comprehensive security coverage suitable for applications handling sensitive data or operating in regulated environments. Includes controls for authentication, authorization, data protection, and secure communications.

The recommendation considers factors such as:

Data sensitivity and classification levels
Regulatory and compliance requirements (GDPR, HIPAA, PCI-DSS, etc.)
Threat landscape and risk assessment from threat modeling
Business criticality and potential impact of security incidents

All security controls referenced in this document align with this recommended compliance level.

6.2. Requirements Mapping

This section maps each high-level requirement to specific security controls from multiple standards (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed descriptions, relevance explanations, and integration guidance. Controls are grouped by standard for clarity.

6.2.1. REQ-001: User registration and login with email/password and social OAuth (Google, GitHub)

OWASP ASVS Controls

ASVS 2.1

Requirement: Verify all authentication functions require a username and password, and sessions are established using secure session tokens. Passwords are validated using secure hashing and salting algorithms.

Relevance: Directly applies to email/password registration and login; ensures session tokens and password storage use secure methods. It sets baseline authentication security requirements for any user login functionality.

Integration Tips: Use strong hashing (e.g., bcrypt/argon2) with per-user salts and enforce secure session tokens with proper attributes (HttpOnly, Secure, SameSite). Ensure registration flows validate inputs and do not leak account existence via timing/errors.

Verification Method: Review password hashing configurations, perform code inspection of authentication code, and run authentication flow tests including session token attribute checks.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

IA-5

Requirement: Authenticator Management: Organizations manage information system authenticators by establishing, issuing, and revoking authenticators such as passwords and tokens.

Relevance: Covers lifecycle of authenticators for email/password and OAuth tokens; ensures proper issuance and revocation processes exist. Important for handling account compromises and token revocation for social logins.

Integration Tips: Implement centralized authenticator lifecycle management including password reset, rotation, and revocation for OAuth tokens. Integrate with identity provider APIs to revoke tokens and maintain revocation logs.

Verification Method: Inspect authenticator management procedures, test token revocation flows with providers, and review logs showing issuance and revocation events.

Priority: Critical

ISO 27001:2022 Controls

A.9.2.3

Requirement: User access provisioning should be implemented based on business and information security requirements. Access rights shall be reviewed regularly.

Relevance: Ensures that user accounts created via registration are provisioned with appropriate rights and subject to periodic review. Applies to initial account creation and social/OAuth-linked accounts.

Integration Tips: Implement user provisioning workflows that assign minimal default privileges and include periodic access reviews. Link account lifecycle events to provisioning records for auditability.

Verification Method: Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews.

Priority: High

6.2.2. REQ-002: Single Sign-On (SSO) via OIDC and SAML for enterprise customers

OWASP ASVS Controls

ASVS 2.6

Requirement: Applications that support federated authentication must validate identity tokens using the provider’s public keys and verify all token claims and audiences.

Relevance: Direct requirement for OIDC/SAML validation; enforces cryptographic validation of tokens and checking of claims to prevent impersonation. Essential for secure SSO.

Integration Tips: Use well-maintained libraries to validate ID tokens and SAML assertions, fetch and cache provider public keys (JWKS), and verify audience, issuer, expiry, and nonce parameters.

Verification Method: Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

IA-2(1)

Requirement: Use of SAML and other federated identity attributes for authentication must ensure assertions are validated and replay-protected.

Relevance: Specifies validation and replay protection for SAML/OIDC assertions which is directly applicable to SSO. Prevents assertion replay and misuse of federated credentials.

Integration Tips: Implement audience restriction, timestamp checks, one-time-use/nonces, and verify assertion freshness. Employ secure channels for metadata exchange and rotate keys as necessary.

Verification Method: Review SAML/OIDC library configurations, test replay attempts, and verify assertion validation logic and timestamps.

Priority: Critical

ISO 27001:2022 Controls

A.9.4.2

Requirement: Where required, secure log-on procedures shall be implemented, and authentication mechanisms should support federated identity when used by the organization.

Relevance: Provides organizational control requiring secure log-on and allowance for federated identity; aligns policy with SSO implementation. Ensures SSO is integrated into secure access policies.

Integration Tips: Update access control policies to include federation; document how SSO providers are approved and how authentication is logged. Ensure training and procedures for SSO onboarding.

Verification Method: Review policy documents, SSO onboarding records, and evidence that secure log-on procedures are followed (logs, configurations).

Priority: High

6.2.3. REQ-003: Role-based access control (Admin, Project Manager, Data Scientist, Viewer) and team workspaces

OWASP ASVS Controls

ASVS 5.1

Requirement: Verify that role-based access control enforces least privilege and separation of duties; authorization checks must be enforced server-side.

Relevance: Directly applicable to implementing RBAC and enforcing server-side checks for roles like Admin and Data Scientist in team contexts. Prevents client-side bypass.

Integration Tips: Design RBAC with clearly defined roles and permissions, implement centralized server-side policy enforcement, and model team workspace scoping for resource access.

Verification Method: Access control tests (attempt privilege escalation), code review for server-side enforcement, and role permission matrices verification.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-2

Requirement: Account Management: The organization manages system accounts, including owner, group, and role assignments and privileges.

Relevance: Describes account and role management processes required to implement RBAC and team workspaces, ensuring accounts and roles are managed consistently.

Integration Tips: Define role lifecycle procedures (creation, modification, removal), standardize role definitions, and integrate with identity management for teams and groups.

Verification Method: Review account management procedures, audit role assignment logs, and sample checks of group/role membership.

Priority: High

ISO 27001:2022 Controls

A.9.1.2

Requirement: Access to systems and services should be controlled by access control mechanisms and role definitions appropriate to user responsibilities.

Relevance: Reinforces requirement to restrict access according to roles and responsibilities; applicable to team workspace access and segregation.

Integration Tips: Map business roles to system roles, document access matrices, and include role-based controls in access control policy. Conduct periodic access reviews.

Verification Method: Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions.

Priority: High

6.2.4. REQ-004: User profile management with avatar uploads, preferences, and 2FA support

OWASP ASVS Controls

ASVS 4.0

Requirement: Applications must support multi-factor authentication for sensitive functions and provide secure mechanisms for managing user profile data.

Relevance: Directly applicable to adding 2FA to profiles and ensuring profile data management is secure. Emphasizes MFA for sensitive actions and proper handling of profile data.

Integration Tips: Offer TOTP/WebAuthn options for 2FA, protect profile endpoints with strong authentication, and require re-authentication for sensitive profile changes. Store preferences securely.

Verification Method: Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

IA-2

Requirement: Identification and authentication (organizational users) – The system uniquely identifies and authenticates organizational users; multi-factor authentication is required for high-risk operations.

Relevance: Defines MFA requirements and unique identification which directly apply to profile management and sensitive preferences or avatar uploads that could introduce risk.

Integration Tips: Apply MFA for high-risk profile operations (credential changes, sensitive preferences) and ensure user ID uniqueness and secure authentication storage for recovery processes.

Verification Method: Review MFA usage logs, perform attempts to bypass MFA, and validate uniqueness checks in authentication flows.

Priority: High

ISO 27001:2022 Controls

A.9.4.1

Requirement: Access to information and application functions should be restricted and managed, including user profile information; media and file handling considerations apply to uploads.

Relevance: Covers restrictions on profile data and file uploads such as avatars; highlights need to manage and restrict access to uploaded media.

Integration Tips: Implement file upload restrictions (type/size), store files on segregated storage with restricted permissions, and apply virus scanning and metadata stripping.

Verification Method: Inspect file handling code, test upload vectors, and verify access controls to stored avatars and profile data.

Priority: High

6.2.5. REQ-005: Create and organize ML projects with metadata, tags, and access controls

OWASP ASVS Controls

ASVS 5.7

Requirement: Metadata and object-level access control must be enforced; access control policies should consider object attributes like tags and ownership.

Relevance: Directly relevant for project and metadata-level access controls so that tagging and metadata do not bypass authorization.

Integration Tips: Implement attribute-based access control (ABAC) where tags/metadata influence policy evaluation; ensure server-side enforcement of object-level permissions.

Verification Method: Test access to objects with different tag/ownership combinations and review policy evaluation logs.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-3

Requirement: Access Enforcement: The information system enforces approved authorizations for logical access to information and system resources in accordance with applicable access control policies.

Relevance: Ensures that access control policies for projects and metadata are enforced consistently across the system.

Integration Tips: Centralize enforcement points for project resources, ensure policies are well-defined and consistently applied across APIs and UI, and log enforcement decisions.

Verification Method: Review enforcement points, run policy conformance tests, and inspect authorization logs for policy application.

Priority: High

ISO 27001:2022 Controls

A.8.1.1

Requirement: Assets associated with information and information processing facilities should be identified and an inventory maintained, including classification and ownership.

Relevance: Project and dataset artifacts are information assets; identifying owners and classification supports correct access controls and handling.

Integration Tips: Maintain an inventory of projects and assigned owners; classify projects by sensitivity to drive access control rules and retention policies.

Verification Method: Inspect the asset inventory and mapping of owners/classifications to access controls.

Priority: Medium

6.2.6. REQ-006: Experiment tracking with versioning, metrics, artifacts, visualizations and comparison UI

OWASP ASVS Controls

ASVS 10.3

Requirement: Protect data integrity and provide mechanisms for versioning and tamper-evidence of critical artifacts and datasets.

Relevance: Addresses need for integrity and tamper-evidence for experiment artifacts and metrics to ensure trustworthiness of tracked experiments.

Integration Tips: Use content-addressable storage or checksums to detect tampering, sign critical artifacts, and maintain immutable versioned histories for artifacts and metrics.

Verification Method: Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

CM-3

Requirement: Configuration Change Control: The organization develops, documents, and maintains baselines and tracks changes to system components and artifacts.

Relevance: Applies to tracking experiment configurations and versions as part of configuration management to ensure traceability and controlled changes.

Integration Tips: Maintain baselines for experiments and configurations, track changes with metadata (who, when, why), and integrate change control into comparison UI.

Verification Method: Review change tracking records, test ability to view diffs between versions, and confirm baselines exist for experiments.

Priority: High

ISO 27001:2022 Controls

A.12.5.1

Requirement: Changes to information processing facilities and systems should be controlled to prevent unauthorized changes.

Relevance: Ensures experiment changes and artifact updates go through controlled change processes to avoid unauthorized modifications.

Integration Tips: Require approvals for production-affecting experiment runs, log changes to experiment metadata, and integrate change tickets with experiment promotions.

Verification Method: Review change management tickets tied to experiments and audit logs for unauthorized modifications.

Priority: Medium

6.2.8. REQ-008: Activity feed and audit logs for project and system changes

OWASP ASVS Controls

ASVS 11.1

Requirement: Application must generate logs for security-relevant events and ensure logs cannot be tampered with; include user actions, admin actions and data changes.

Relevance: Directly mandates logging of user and admin actions relevant to activity feeds and audit trails to support accountability and forensics.

Integration Tips: Log critical events with non-repudiation mechanisms (append-only storage, HMAC) and separate privileges for log access; include contextual metadata (user, project, action).

Verification Method: Inspect logs for required events, attempt tampering to validate protections, and verify retention/rotation policies.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AU-2

Requirement: Audit Events: The information system must determine and record auditable events and provide a record of actions taken.

Relevance: Specifies which events to audit and records to maintain, directly applicable to activity feed and audit logs design.

Integration Tips: Define auditable events per system components (project changes, permissions changes), ensure timestamps and actor IDs are recorded, and protect audit records.

Verification Method: Review audit event definitions, inspect sample logs, and validate completeness against event list.

Priority: Critical

ISO 27001:2022 Controls

A.12.4.1

Requirement: Event logs recording user activities, exceptions, and information security events should be produced and retained.

Relevance: Reinforces need for event logging and retention policies covering system and project changes for compliance and incident response.

Integration Tips: Define retention periods, secure log storage, and access controls for log review. Integrate logs into SIEM for monitoring and alerting.

Verification Method: Check log retention configurations, review access controls to logs, and confirm logs include required event types.

Priority: High

6.2.9. REQ-009: Conversational AI assistant (LLM) with chat UI, multi-language support and saved chat history

OWASP ASVS Controls

ASVS 10.1

Requirement: Sensitive data processed by application features like chat should be classified and protected; stored chat history must be access-controlled and encrypted.

Relevance: Directly relevant to protecting chat transcripts and LLM interactions which may include PII or sensitive project context.

Integration Tips: Encrypt chat histories at rest, apply access controls based on roles and project context, classify content and redact or mask sensitive items prior to storage.

Verification Method: Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

PL-8

Requirement: Information Security Architecture: Ensure that system design includes protections for privacy and confidentiality of user-generated content and retains accountability.

Relevance: Requires architectural consideration for LLM components to protect privacy and ensure accountability for generated/stored content.

Integration Tips: Design the LLM integration with privacy-by-design principles, include data minimization, and log model inputs/outputs for accountability while protecting sensitive items.

Verification Method: Review architecture diagrams, validate data flows, and confirm presence of privacy controls and logging/monitoring for LLM services.

Priority: High

ISO 27001:2022 Controls

A.18.1.4

Requirement: Personal data shall be protected in accordance with relevant legislation and organizational policies, including chat transcripts.

Relevance: Ensures chat history and personal data processed by the assistant are protected in line with legal/regulatory obligations.

Integration Tips: Implement user consent mechanisms, define retention for chat data, and provide controls for export/deletion to meet data subject rights.

Verification Method: Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts.

Priority: High

6.2.10. REQ-010: Natural-language search across experiments, models, datasets and chat history

OWASP ASVS Controls

ASVS 9.3

Requirement: Search functionality must enforce access control checks on results and avoid information leakage via search suggestions or metadata.

Relevance: Search across sensitive artifacts must not return unauthorized results; prevents leakage via NLP search suggestions or indexed metadata.

Integration Tips: Ensure search index enforces row-level security, filter results by user/project permissions, and sanitize suggestion engines to avoid exposing counts or metadata of inaccessible items.

Verification Method: Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-19

Requirement: Access Control for Mobile Devices and Remote Access: Limitations and protections on information retrieval mechanisms must be enforced to prevent unauthorized disclosure.

Relevance: Mandates protective controls for remote search and retrieval mechanisms to prevent unauthorized disclosure; applies to natural-language search.

Integration Tips: Apply authentication/authorization on search APIs, rate-limit queries to mitigate enumeration, and log search access for auditing.

Verification Method: Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns.

Priority: High

ISO 27001:2022 Controls

A.9.2.5

Requirement: Access rights should be reviewed to ensure that search and retrieval functions do not expose data beyond authorized users.

Relevance: Supports ongoing verification that search permissions align with assigned access rights and prevent inadvertent exposure.

Integration Tips: Include search access checks in periodic access reviews and adjust index/build processes when access rights change.

Verification Method: Check results of access reviews, and validate that changes to permissions update search index behavior.

Priority: Medium

6.2.11. REQ-011: AI-powered code suggestions, explanations, experiment summaries, and automated insights

OWASP ASVS Controls

ASVS 10.4

Requirement: AI-generated outputs that may expose sensitive information must be sanitized and the model prompts/outputs should be logged and controlled for confidentiality.

Relevance: Directly relevant: AI outputs can leak secrets or IP; this control mandates sanitization and control over model prompts/outputs to prevent leakage.

Integration Tips: Implement output filters that detect and redact secrets, log prompts/outputs for review with access controls, and apply model input sanitization to avoid leaking sensitive content.

Verification Method: Review logs to ensure prompt/output recording and filtering, test the system by feeding sensitive prompts and verifying redaction.

Level: L3 | Priority: Critical

NIST SP 800-53 Controls

SI-11

Requirement: Error Handling and Information Leakage: Applications must detect and protect against unauthorized dissemination of information via outputs and generated content.

Relevance: Requires detection and prevention of output-based leakage, applicable when code suggestions or summaries could reveal sensitive or proprietary data.

Integration Tips: Add detectors for confidential patterns (API keys, secrets) in AI outputs, restrict model capabilities for certain projects, and implement human review for high-risk outputs.

Verification Method: Conduct tests that confirm detection of secret patterns, review human-in-the-loop approvals, and audit output logs for leakage incidents.

Priority: High

ISO 27001:2022 Controls

A.9.4.4

Requirement: Controls should be applied to tools and utilities that can access sensitive information, including AI tools that generate or transform data.

Relevance: Ensures AI tooling that can access or generate sensitive content is treated like privileged utilities and controlled accordingly.

Integration Tips: Restrict AI tool access with least privilege, log usage by identity/project, and require approvals for access to datasets or models that produce sensitive outputs.

Verification Method: Review access controls for AI tooling, usage logs, and approval workflows for privileged AI features.

Priority: Medium

6.2.12. REQ-012: Context-aware help respecting current project context and user role

OWASP ASVS Controls

ASVS 5.2

Requirement: Contextual features must honor authorization policies and ensure that help or guidance does not reveal unauthorized data or operations.

Relevance: Context-aware help must not reveal information outside a user’s role; enforces that help respects authorization boundaries.

Integration Tips: Ensure help content generation queries use the same authorization layer as UI views; mask or suppress details the user is not authorized to see.

Verification Method: Test help outputs in various roles/projects and confirm no unauthorized data is revealed.

Level: L2 | Priority: High

NIST SP 800-53 Controls

AC-6(1)

Requirement: Least Privilege Enforcement: Systems that provide context-based capabilities must apply least privilege to contextual data and actions.

Relevance: Reinforces applying least privilege to contextual help and preventing exposure of higher-privilege details through help features.

Integration Tips: Implement scoping of contextual help queries to the user’s effective permissions and avoid aggregating information across projects unless permitted.

Verification Method: Perform privilege escalation tests using contextual help and log analysis to ensure scoping is honored.

Priority: Medium

ISO 27001:2022 Controls

A.7.1.2

Requirement: Users must be made aware of their security responsibilities; context-aware help should align with these policies.

Relevance: Ensures help does not contradict organizational security policies and that user-facing guidance reinforces security responsibilities.

Integration Tips: Align help text with security policies and include reminders of responsibilities in context-aware tips, especially for privileged actions.

Verification Method: Review help content against policy and gather user feedback on compliance alignment.

Priority: Low

6.2.13. REQ-013: Chat export and per-user chat history controls (export/delete)

OWASP ASVS Controls

ASVS 10.6

Requirement: Provide mechanisms for users to export their data and request deletion; systems must verify authenticity of requests and ensure propagation.

Relevance: Directly applies to chat export and deletion flows, enforcing verification and safe execution of these operations.

Integration Tips: Implement authenticated export endpoints with rate limits and data packaging, and ensure deletion routines remove data from primary and backup stores where required.

Verification Method: Test export and deletion flows end-to-end including backups, and verify authenticity checks and propagation to dependent systems.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

MP-6

Requirement: Media Sanitization: Ensure data is properly removed from storage when deletion/export requests are fulfilled.

Relevance: Ensures deleted chat history is sanitized from all media/storage mediums and not left recoverable on backups or caches.

Integration Tips: Design deletion to mark records for sanitization and run secure deletion or cryptographic erasure where possible; track deletion tasks to completion.

Verification Method: Inspect sanitization procedures, attempt to recover deleted chat artifacts from backups, and verify logs of completed sanitization.

Priority: High

ISO 27001:2022 Controls

A.18.1.4

Requirement: Implement procedures to support privacy rights, including data access, export, and deletion consistent with legislation.

Relevance: Mandates support for data subject rights including export and deletion applicable to chat histories and per-user controls.

Integration Tips: Provide transparent processes and timelines for export/delete, authenticate requestors, and document compliance with legal obligations.

Verification Method: Review data subject request procedures, test sample export-delete requests, and validate audit trail of actions.

Priority: High

6.2.14. REQ-014: Dataset upload, preview (tables/images/structured data), and data versioning/lineage

OWASP ASVS Controls

ASVS 8.5

Requirement: File uploads must be validated, stored securely, scanned for malware, and access-controlled; previews should not expose underlying storage metadata.

Relevance: Directly applies to dataset upload and preview functionality to prevent malware, exfiltration or leakage via previews.

Integration Tips: Validate file types and sizes, run malware scanning and content inspection, strip metadata before preview, and store uploads in isolated, permissioned buckets.

Verification Method: Conduct upload fuzzing, verify malware scanning logs, and test metadata stripping on previews.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SI-3

Requirement: Malicious Code Protection: The information system must employ mechanisms to detect and prevent malicious code from uploaded content.

Relevance: Covers preventing malicious content in uploaded datasets which is critical to protect processing and preview systems.

Integration Tips: Integrate AV and sandboxing for uploaded files, apply content-type validation, and restrict processing of untrusted file types.

Verification Method: Review AV/sandbox integration logs and simulate uploads of malicious artifacts to verify protection.

Priority: High

ISO 27001:2022 Controls

A.12.3.1

Requirement: Backup and versioning processes should be implemented to ensure data integrity and availability; provenance and lineage should be traceable.

Relevance: Supports data versioning/lineage and ensures datasets are backed up and provenance recorded for traceability and recovery.

Integration Tips: Implement versioned storage for datasets, capture lineage metadata on ingest, and ensure backups include provenance metadata while respecting retention policies.

Verification Method: Review versioning strategy, lineage metadata records, and backup integrity checks.

Priority: Medium

6.2.15. REQ-015: Interactive metric visualization and drag-and-drop custom dashboards with export (image/PDF)

OWASP ASVS Controls

ASVS 9.1

Requirement: Ensure all data displayed in dashboards is properly encoded and rendered to prevent XSS and injection vulnerabilities.

Relevance: Dashboards that render user-supplied or dataset-derived content are susceptible to XSS; encoding is essential for secure visualization.

Integration Tips: Apply context-sensitive output encoding for HTML, SVG, and JavaScript contexts, sanitize user-supplied widgets, and restrict allowed widget scripts.

Verification Method: Run XSS tests on dashboard inputs/exports and perform code review of rendering pipelines.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SC-7

Requirement: Boundary Protection: Ensure secure processing and rendering boundaries to avoid unauthorized data leakage during export processes.

Relevance: Ensures exports (PDF/image) do not leak hidden metadata or unintentional content; boundary protections prevent cross-tenant leakage.

Integration Tips: Render exports in isolated environments, strip metadata, and validate export content against user permissions before delivery.

Verification Method: Inspect exported files for metadata, test cross-tenant export attempts, and validate isolation mechanisms.

Priority: High

ISO 27001:2022 Controls

A.14.2.8

Requirement: Applications and systems providing visualization must be tested for security vulnerabilities as part of development lifecycle.

Relevance: Mandates testing of dashboard features for security vulnerabilities to prevent exploitation in visualization/export features.

Integration Tips: Include security tests in CI for dashboard components, such as SAST/DAST for rendering engines, and perform export content reviews.

Verification Method: Review security test results, CI pipelines, and results of vulnerability scans for dashboard modules.

Priority: Medium

6.2.16. REQ-016: Model registry with metadata, versioning, comparison, and model card generation

OWASP ASVS Controls

ASVS 10.3

Requirement: Maintain integrity and provenance of models and associated metadata; model artifacts should be versioned and access-controlled.

Relevance: Directly relevant for model registry to ensure integrity, provenance, and access control are enforced for models and their metadata.

Integration Tips: Use signed artifacts, maintain immutable model versions, record provenance metadata (training data, hyperparameters), and protect model storage with ACLs.

Verification Method: Verify artifact signatures, examine provenance metadata, and test access control enforcement on model artifacts.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

CM-8

Requirement: Information System Component Inventory: The organization tracks and documents components including models and their versions.

Relevance: Requires inventory and documentation of models, which supports registry capabilities and tracking of model versions and components.

Integration Tips: Maintain an authoritative registry listing models, versions, owners, and dependencies; integrate inventory updates with CI/CD for model promotion.

Verification Method: Review model inventory, sampling to confirm metadata accuracy, and check synchronization with deployment pipelines.

Priority: High

ISO 27001:2022 Controls

A.8.2.1

Requirement: Information assets including models should be classified and protected according to sensitivity and business value.

Relevance: Model cards and registry metadata should be classified to inform protection levels for models (e.g., proprietary vs public).

Integration Tips: Classify models and apply appropriate access controls and handling rules; include classification in model cards and registry metadata.

Verification Method: Review classification labels in registry, confirm enforcement of handling rules, and audit access to classified models.

Priority: Medium

6.2.17. REQ-017: Deploy models to staging and production via UI and support A/B testing and rolling deployments

OWASP ASVS Controls

ASVS 14.1

Requirement: Deployment pipelines must be secured, use signed artifacts, and support rollback; environment segregation should prevent accidental promotion of unapproved artifacts.

Relevance: Ensures secure deployment processes for models with signatures and environment segregation to prevent unauthorized promotion to production.

Integration Tips: Require artifact signing and verification during deployments, implement separate credentials for staging vs production, and provide safe rollback mechanisms.

Verification Method: Review pipeline configs for signature verification, test rollback and promotion flows, and inspect environment segregation.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SA-9

Requirement: External System Services: Ensure security considerations for deployment and operational procedures including CI/CD and testing environments.

Relevance: Advises on inclusion of security in acquisition and operation of deployment tooling, relevant for A/B testing and rolling deployments.

Integration Tips: Document security requirements for deployment systems, ensure CI/CD credentials are secured and rotate secrets, and isolate A/B testing environments.

Verification Method: Review CI/CD security settings, secret storage, and evidence of security requirements in procurement or configuration.

Priority: High

ISO 27001:2022 Controls

A.12.1.2

Requirement: Changes to systems, including deployments, must follow change management procedures and be authorized prior to implementation.

Relevance: Ensures deployments (model promotions, A/B rollouts) follow approved change management to avoid accidental or unauthorized production changes.

Integration Tips: Integrate deployment UI actions with change approval workflows and retain audit trails for deployment approvals and rollouts.

Verification Method: Inspect change tickets linked to deployments and verify approvals/logs for production promotions.

Priority: High

6.2.18. REQ-018: Model monitoring dashboard (accuracy, drift, latency) and alerting

OWASP ASVS Controls

ASVS 11.3

Requirement: Systems should monitor operational metrics and security-relevant metrics and generate alerts for anomalous behavior, including model drift indicators.

Relevance: Directly applicable for model monitoring to detect drift or anomalous performance which could indicate data or model issues.

Integration Tips: Instrument model telemetry, define thresholds for alerts, route alerts to incident management, and ensure telemetry is access-controlled and tamper-resistant.

Verification Method: Review telemetry collection, simulate drift conditions and verify alerting, and inspect alert handling records.

Level: L2 | Priority: High

NIST SP 800-53 Controls

SI-4

Requirement: Information System Monitoring: The system must detect and report security-relevant events and anomalies.

Relevance: Mandates monitoring and detection which align with model monitoring dashboards to capture anomalies and incidents.

Integration Tips: Integrate monitoring outputs with SIEM/incident systems, ensure metrics are correlated with security events, and maintain monitoring coverage for key performance indicators.

Verification Method: Check monitoring coverage, correlation rules, and incident logs for model-related alerts.

Priority: High

ISO 27001:2022 Controls

A.16.1.1

Requirement: Responsibilities and procedures for incident detection and reporting should be established, including monitoring systems for service performance and security incidents.

Relevance: Ensures there are procedures for responding to monitoring alerts (e.g., model drift) as part of incident management.

Integration Tips: Define owner responsibilities for monitoring alerts, document response playbooks for model issues, and train on escalation paths.

Verification Method: Review incident response plans and perform tabletop exercises for model-monitoring incidents.

Priority: Medium

6.2.19. REQ-019: Commenting, threaded discussions, @mentions and real-time collaboration on projects

OWASP ASVS Controls

ASVS 9.4

Requirement: User-generated content must be sanitized and encoded to prevent XSS; real-time components must protect against injection and CSRF.

Relevance: Applies to comments/threaded content and @mentions to prevent injection attacks and cross-site scripting in collaboration features.

Integration Tips: Sanitize and encode messages for display contexts, enforce CSRF protections on actions, and limit allowed markup or provide a safe subset for rich text.

Verification Method: Perform XSS and injection testing on commenting and mention features and review sanitization libraries used.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SC-13

Requirement: Cryptographic Protection: Communications and collaboration channels should be protected to ensure confidentiality and integrity.

Relevance: Real-time collaboration requires protection of message channels to prevent eavesdropping or tampering, ensuring integrity of threaded discussions.

Integration Tips: Use TLS for transport, apply end-to-end protections where feasible, and authenticate message sources to prevent impersonation via mentions.

Verification Method: Inspect TLS configurations, review message authentication mechanisms, and verify integrity protections.

Priority: High

ISO 27001:2022 Controls

A.6.2.1

Requirement: Policies for use of collaboration tools and acceptable use should be defined and enforced.

Relevance: Ensures policies governing use of collaboration tools are in place and applied to protect project discussions and collaboration.

Integration Tips: Define acceptable use policies for collaboration, include guidance on sharing sensitive information in comments, and enforce via training and technical controls.

Verification Method: Review policies and conduct audits of collaboration use for policy compliance.

Priority: Low

6.2.21. REQ-021: Integration adapters for cloud storage (S3/GCS/Azure), external ML platforms and CI/CD

OWASP ASVS Controls

ASVS 12.1

Requirement: Integrations to cloud storage and external services must use strong authentication, least privilege, and secure credential handling (e.g., short-lived tokens).

Relevance: Directly pertains to adapters connecting to S3/GCS/Azure and other platforms: requires secure auth and credential management.

Integration Tips: Use short-lived credentials (STS), role-based access, and avoid long-lived secrets; rotate credentials and store them in a secrets manager.

Verification Method: Inspect credential handling, verify use of short-lived tokens, and review permissions granted to adapter service accounts.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-17

Requirement: Remote Access: Control and secure remote connections to external services and cloud resources; enforce strong authentication and encryption.

Relevance: Applies to remote connections to cloud services used by adapters and demands secure channels and authentication.

Integration Tips: Enforce TLS for all remote connections, mandate mutual TLS where appropriate, and log remote access for audit.

Verification Method: Review network/TLS configurations and logs of remote connections to cloud services.

Priority: High

ISO 27001:2022 Controls

A.14.2.7

Requirement: Where functions are outsourced or integrated externally, security requirements must be defined and enforced, including credential and access controls.

Relevance: If adapters integrate with external services or third-party platforms, security requirements for those relationships must be established.

Integration Tips: Define SLAs/security requirements for integrations and ensure secure onboarding of third-party connectors with scoped access and audits.

Verification Method: Review third-party agreements, connector security assessments, and integration onboarding documentation.

Priority: Medium

6.2.22. REQ-022: Secure APIs for artifact storage, model deployment, and telemetry ingestion

OWASP ASVS Controls

ASVS 14.3

Requirement: APIs must authenticate and authorize requests, validate input, enforce rate-limiting, use TLS, and protect against common API attacks.

Relevance: Directly covers security expectations for APIs handling artifacts, deployments, and telemetry ingestion to prevent abuse and data compromise.

Integration Tips: Enforce token-based auth (OAuth2/JWT) with scopes, validate all inputs and payloads, apply rate limits, and require TLS with strong ciphers.

Verification Method: API security testing (fuzzing, auth bypass tests), review TLS configs, and examine rate-limit enforcement logs.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

SC-13

Requirement: Cryptographic Protection: Protect API communications and sensitive data in transit with approved cryptography.

Relevance: Mandates cryptographic protection of API communications, essential for telemetry and artifact transfers containing sensitive info.

Integration Tips: Use TLS for transport, encrypt payloads where necessary, and manage keys with a KMS. Avoid weak cipher suites and protocol versions.

Verification Method: Review TLS termination points, cipher suite lists, and key management practices.

Priority: High

ISO 27001:2022 Controls

A.9.2.6

Requirement: Use of privileged interfaces and APIs must be restricted and controlled with appropriate authentication and authorization.

Relevance: APIs for deployment/ingestion may provide privileged operations; this control ensures their usage is restricted and audited.

Integration Tips: Segment privileged API endpoints, require higher assurance (MFA, IP allowlists) for privileged ops, and log/monitor privileged API calls.

Verification Method: Review access controls for privileged endpoints, inspect logs of privileged calls, and test enforcement of additional protections.

Priority: High

6.2.23. REQ-023: Data retention, export, deletion and data subject rights workflows for compliance

OWASP ASVS Controls

ASVS 10.6.2

Requirement: Data retention and deletion policies must be enforced programmatically; provide mechanisms to export and delete personal data in accordance with legal requirements.

Relevance: Directly applicable to retention/export/deletion and data subject workflows supporting regulatory compliance (e.g., GDPR).

Integration Tips: Implement automated retention enforcement, authenticated export endpoints, and deletion flows that remove data from live and archived stores as required.

Verification Method: Test retention enforcement, perform data export requests, and validate deletion propagation and logs.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

MP-6

Requirement: Media Sanitization: Ensure secure disposal and sanitization of media and data when no longer required.

Relevance: Covers secure sanitization for deleted data and ensures exports/deletions actually remove data from all media including backups.

Integration Tips: Document sanitization procedures for primary and backup storage, use cryptographic erasure where possible, and validate deletion completion.

Verification Method: Perform recovery attempts on sanitized media, and review sanitization logs and procedures.

Priority: High

ISO 27001:2022 Controls

A.18.1.4

Requirement: Organizations must adhere to legal and regulatory requirements relating to data retention and subject rights.

Relevance: Mandates alignment with legal obligations for retention and data subject rights, applying to export and deletion workflows.

Integration Tips: Maintain a legally-aligned retention schedule, provide clear DSAR (data subject access request) processes, and log all DSAR activities.

Verification Method: Review retention schedules, test DSAR handling, and audit logs for DSAR fulfillment.

Priority: Critical

6.2.24. REQ-024: Audit, logging, encryption, and monitoring for security and operational observability

OWASP ASVS Controls

ASVS 11.2

Requirement: Implement sufficient logging, secure log storage, and monitoring to detect security incidents; ensure logs are integrity-protected and access-controlled.

Relevance: Directly addresses core requirements for logging, encryption of logs, and monitoring to support security and operations observability.

Integration Tips: Protect logs with write-once or HMAC integrity, centralize logs in SIEM, restrict log access, and encrypt logs at rest and in transit.

Verification Method: Inspect log integrity protections, review SIEM alerts, and test log access controls.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

IA-5(1)

Requirement: Use of cryptographic authentication mechanisms and key management controls to protect secrets and authentication tokens.

Relevance: Key management and cryptography underpin secure storage of secrets, encryption of telemetry, and protection of logs and artifacts.

Integration Tips: Use a centralized KMS for key lifecycle, protect authentication tokens using hardware-backed storage where possible, and rotate keys regularly.

Verification Method: Review key management practices, KMS configuration, and evidence of key rotation and access controls.

Priority: High

ISO 27001:2022 Controls

A.10.1.1

Requirement: Use cryptographic controls to protect confidentiality, integrity of information and manage keys appropriately.

Relevance: Provides policy-level mandate to use cryptography for encrypting sensitive logs, telemetry, artifacts and for overall observability data protection.

Integration Tips: Create cryptography policy, select approved algorithms, and integrate cryptographic controls into logging and telemetry pipelines with KMS support.

Verification Method: Review cryptography policy and check that deployed systems use approved algorithms and key management according to policy.

Priority: High

6.2.25. REQ-025: Administrative features: tenant settings, billing integration, usage reporting, and quota management

OWASP ASVS Controls

ASVS 5.5

Requirement: Administrative interfaces must be protected with high assurance authentication, authorization, and auditability; separate admin functions from normal user functions.

Relevance: Administrative tenant/billing interfaces require high protection to avoid misuse or fraudulent changes; separation from user functions is crucial.

Integration Tips: Enforce MFA for admin accounts, restrict admin UI by IP or role, segregate admin APIs, and maintain detailed audit logs for all admin actions.

Verification Method: Test admin authentication flows (MFA), review segregation of admin endpoints, and inspect admin action logs.

Level: L2 | Priority: Critical

NIST SP 800-53 Controls

AC-5

Requirement: Separation of Duties: The system must support separation of duties and prevent conflict of interest by restricting access to administrative functions.

Relevance: Prevents a single administrative user from performing incompatible tasks on billing/tenant settings, mitigating risk of fraud or abuse.

Integration Tips: Implement RBAC that separates billing, tenant admin, and audit roles; require dual-control for sensitive billing changes.

Verification Method: Review role separation, test that a single role cannot perform conflicting admin actions, and inspect change logs.

Priority: High

ISO 27001:2022 Controls

A.18.2.3

Requirement: Systems performing administrative or billing tasks should be subject to technical reviews to ensure compliance with security requirements.

Relevance: Mandates technical reviews/audits of admin/billing modules to ensure they meet security and compliance expectations.

Integration Tips: Schedule regular security reviews for admin components, include billing integrations in compliance scans, and remediate findings promptly.

Verification Method: Review technical audit reports and confirm remediation of identified issues.

Priority: Medium

6.3. Cross-Functional Security Controls

The following controls apply globally across all system components:

Logging and Audit

Description: Comprehensive logging of security-relevant events, user actions, admin actions, and system changes with tamper-evidence and retention policies.

Applies to: All requirements that involve user actions, admin operations, data changes, sharing, deployments, AI outputs, and integrations

Implementation Guidance: Centralize logs into a SIEM with access controls, ensure logs are integrity-protected (WORM/HMAC), include contextual metadata (user, project, request-id), and define retention/archival policies aligned with compliance.

Encryption and Key Management

Description: Use approved cryptography to protect data at rest and in transit and manage keys via a centralized KMS with lifecycle controls.

Applies to: Authentication tokens, chat histories, artifacts, model binaries, backups, telemetry, and API communications

Implementation Guidance: Encrypt data at rest with AES-256 or approved alternatives, enforce TLS for transport, use KMS for key rotation and access controls, and implement cryptographic policies and audits.

Access Control and RBAC/ABAC

Description: Centralized authorization system enforcing least privilege, separation of duties, and object-level policies (roles, tags, metadata).

Applies to: User management, projects, datasets, model registry, sharing features, and admin interfaces

Implementation Guidance: Implement server-side enforcement, use ABAC for tag/metadata-based rules, integrate with identity providers for SSO, and perform periodic access reviews and audits.

Input/Output Sanitization and Safe Rendering

Description: Sanitize all inbound content (uploads, comments, templates) and apply context-sensitive encoding for outputs to prevent XSS and injection.

Applies to: File uploads, dashboards, comments, chat, visualizations, exports

Implementation Guidance: Validate and sanitize inputs, use safe rendering libraries for HTML/SVG, restrict allowed markup in user content, and strip metadata from exported artifacts.

Data Subject Rights and Retention Management

Description: Programmatic enforcement of retention policies, and processes supporting export and deletion requests (DSARs) with verification and propagation to backups.

Applies to: Chat history, datasets, user profiles, telemetry, and backups

Implementation Guidance: Provide authenticated DSAR interfaces, maintain audit trails for requests, implement deletion workflows that handle backups, and ensure compliance with legal retention requirements.

6.4. Requirements Traceability Overview

This section demonstrates complete traceability from high-level requirements through threats to security controls and verification methods.

Coverage Summary: Traceability matrix contains 35 requirements. 35 requirements (100.0%) linked to threats. 25 requirements (71.4%) mapped to security controls (OWASP ASVS, NIST SP 800-53, ISO 27001). Coverage: Partial.

Sample Traceability Mappings

The following table shows traceability for high-priority requirements:

Req ID	Requirement	Threats	Security Controls	Standards	Priority	Verification
REQ-001	User registration and login supporting e…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews.
REQ-002	Enterprise Single Sign-On (SSO) via OIDC…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected.
REQ-003	Role-based access control (RBAC) support…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions.
REQ-005	User profile management including avatar…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes.
REQ-006	Project creation and organization with d…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Test access to objects with different tag/ownership combinations and review policy evaluation logs.
REQ-007	Experiment tracking with artifact versio…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity.
REQ-008	Side-by-side experiment comparison UI wi…	9 threats	3 controls	ISO27001, NIST, OWASP	Critical	Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity.
REQ-010	Activity feed and audit logging for proj…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Check log retention configurations, review access controls to logs, and confirm logs include required event types.
REQ-011	Conversational AI assistant accessible v…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts.
REQ-012	Natural language search across experimen…	10 threats	3 controls	ISO27001, NIST, OWASP	Critical	Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls.

Showing 10 of 35 requirements. See Appendix D for complete traceability matrix.

Traceability Statistics

Total Requirements Tracked: 35
Requirements Linked to Threats: 35 (100.0%)
Requirements Mapped to Controls: 25 (71.4%)
Average Controls per Requirement: 2.1
Control Distribution by Standard:
- OWASP ASVS: 25 controls
- NIST SP 800-53: 25 controls
- ISO 27001: 25 controls
Verification Coverage: 100% (all requirements have verification methods)

7. AI/ML Security Requirements

This section addresses security requirements specific to artificial intelligence and machine learning components within the system. AI/ML systems introduce unique security challenges including prompt injection attacks, data poisoning, model theft, adversarial inputs, and bias vulnerabilities. This analysis identifies AI/ML components, assesses their security risks, and prescribes specialized controls to protect both the AI systems themselves and the data they process.

7.1. AI/ML Components Detected

This section identifies all AI/ML components within the system that require specialized security controls.
1. Conversational AI Assistant (LLM): Provides natural language queries to search experiments, code suggestions, and context-aware help using large language models.
2. Experiment Tracking and Insights: Utilizes AI to automate insights and generate summaries from experiment results.
3. Model Monitoring Dashboard: Employs machine learning models to monitor prediction accuracy, drift, and provide A/B testing capabilities.

7.2. AI/ML Threat Model

Component	Identified Threats
Conversational AI Assistant (LLM)	- Prompt injection
	- Adversarial input attacks
	- Data leakage through training data
Experiment Tracking and Insights	- Data leakage (PII in training prompts)
	- Model inversion attacks
Model Monitoring Dashboard	- Model poisoning
	- Supply chain vulnerabilities

7.3. AI/ML Security Controls

Conversational AI Assistant (LLM)

Prompt Injection Prevention: Implement strict validation of user inputs to prevent malicious prompts. Use a whitelist approach to allow only safe commands.
Output Filtering and Sanitization: Sanitize outputs generated by the AI to remove sensitive information and ensure compliance with data protection regulations.
Rate Limiting and Abuse Prevention: Monitor and limit the frequency of requests to the AI assistant to mitigate abuse and ensure fair usage among users.

Experiment Tracking and Insights

Input Validation for AI Inputs: Validate inputs to the AI system to prevent injection attacks and ensure that only expected data formats are processed.
Model Access Controls: Develop strict access controls around who can view or modify experiment data and insights to protect proprietary information.
Monitoring for Adversarial Inputs: Implement monitoring mechanisms to detect unusual patterns or inputs that could indicate adversarial attacks on the model.

Model Monitoring Dashboard

Model Versioning and Rollback Capabilities: Ensure that all models are versioned, allowing rollback to a previous stable state in case of detected anomalies or poisoning.
Supply Chain Security for Models: Implement checks to verify the integrity of models and datasets sourced from external providers to prevent supply chain attacks.
Bias and Fairness Considerations: Regularly audit models for bias and implement fairness checks to ensure equitable outcomes across diverse user groups.

7.4. Integration with Existing Security Controls

AI controls integrate with standard security practices by complementing user authentication methods (like 2FA) to secure access to sensitive AI components. Additionally, regular audits and logging mechanisms established for general application security will also encompass AI-specific logs for monitoring model performance and detecting anomalies. Data retention and compliance workflows for PII will be extended to cover AI-generated data and interactions, ensuring comprehensive protection.

7.5. AI/ML Monitoring Requirements

Monitoring Area	Description
User Interaction Monitoring	Track user interactions with the AI assistant to detect unusual patterns or potential abuse.
Model Performance Monitoring	Continuously monitor model accuracy, drift, and performance metrics to ensure reliability and compliance.
Audit Logging	Maintain detailed logs of all AI-related operations and user interactions for accountability and forensic analysis.
Data Access Auditing	Regularly audit accesses to datasets and results generated by the AI to protect sensitive information.

8. Compliance Requirements

This section identifies regulatory and legal compliance obligations applicable to the system based on data types, geographic scope, industry sector, and business operations. Compliance requirements drive specific security controls, data handling procedures, audit capabilities, and privacy protections. Non-compliance can result in significant legal penalties, reputational damage, and business disruption. This analysis maps applicable regulations to specific security requirements and operational procedures.

8.1. Applicable Regulations

Regulations were identified based on the types of data the AI-Powered ML Workflow Management Web Application will handle, including user information, project data, and potential integration with external systems. The geographic scope includes regions such as the EU and California, which necessitate compliance with specific data protection laws. The industry sector primarily relates to technology and data processing, which is subject to stringent privacy regulations. Business operations include machine learning and data management, further highlighting applicable compliance requirements. Compliance requirements directly impact security controls, data handling procedures, and operational processes.

Regulation	Applicability Reason
GDPR	Applies because the system processes personal data of EU residents, including user registration and profile management.
CCPA	Applies due to the handling of personal data of California residents, requiring transparency and consumer rights.
HIPAA	May apply if health-related data is processed or if the application is used in healthcare settings.
PCI-DSS	Relevant if the application processes payment card data, which could occur during billing integrations.
SOX	Applies if the application handles financial data relevant to public companies, impacting audit and reporting functions.
COPPA	Relevant if any data concerning children under 13 years is collected, necessitating parental consent.
Data Residency Laws	Important for compliance when storing data in specific geographic locations, impacting cloud storage integrations.
GLBA	May apply if the application deals with nonpublic personal information in financial contexts.

8.2. Compliance Controls by Regulation

CCPA

Transparent data collection notices.
Consumer opt-out mechanisms for data selling.
Enhanced rights for consumers to access and delete their data.
Annual reporting on data handling practices.

HIPAA

Secure storage and transmission of health information.
Access controls and audit logs for health data.
Business Associate Agreements with third-party service providers.

PCI-DSS

Secure handling of payment card data.
Implementation of strong access control measures.
Regular security testing and vulnerability management.

SOX

Financial data handling procedures for accuracy and security.
Regular audits and internal controls documentation.
Retention of financial records and communications.

COPPA

Parental consent verification mechanisms.
Age verification processes for child user accounts.
Clear privacy policies regarding children’s data.

Data Residency Laws

Data localization practices based on jurisdictional requirements.
Compliance with international data transfer regulations.

GLBA

Protection of nonpublic personal information.
Privacy notices and consumer opt-out rights.

8.3. Data Subject Rights

Right	Description
Right to Access	Users can request access to their personal data.
Right to Rectification	Users can request correction of inaccurate personal data.
Right to Erasure	Users can request deletion of their personal data.
Right to Data Portability	Users can request a copy of their personal data in a portable format.
Right to Object	Users can object to the processing of their personal data.
Right to Withdraw Consent	Users can withdraw consent for data processing at any time.

8.4. Privacy Requirements

Consent: Users must provide explicit consent for data collection and processing.
Privacy Notices: Clear and accessible privacy notices must be provided to users detailing data handling practices.
Data Protection Impact Assessment (DPIA): Conduct DPIAs to assess risks associated with data processing activities.

8.5. Audit and Monitoring Requirements

Logging: Maintain detailed logs of data access and modifications to ensure traceability.
Monitoring: Continuous monitoring of systems for security breaches and compliance violations.
Audit Trails: Maintain audit trails for all data processing activities to support compliance audits.

8.6. Data Handling Rules

Retention: Personal data must be retained only as long as necessary for its intended purpose.
Deletion: Clear procedures for the secure deletion of personal data upon request or when no longer needed.
Data Minimization: Collect only the data necessary for the application’s functionality and purposes.

8.7. Compliance Risk Assessment

Compliance risks associated with the application include potential breaches of user data, inadequate consent mechanisms, and non-compliance with financial reporting requirements. Addressing these risks is crucial for maintaining user trust and meeting legal obligations.

Key Compliance Risks:

Risk of data breaches leading to unauthorized access to personal data.
Risk of inadequate user consent management resulting in non-compliance.
Risk of failing to meet financial data handling requirements under SOX and GLBA.

9. Security Architecture Recommendations

This section provides comprehensive security architecture guidance that integrates security controls into the system’s technical design. Security architecture defines how security principles, controls, and patterns are applied across system components to create a cohesive, defense-in-depth security posture. The recommendations address architectural principles, component-level controls, data protection strategies, and third-party integration security to ensure security is built into the system design.

9.1. Architectural Security Principles

Architectural security principles provide the foundational philosophy guiding all security design decisions. These principles ensure a consistent security posture across system components, drive selection and implementation of controls, and enable defensible, auditable security outcomes that scale with multi-tenant, cloud-hosted ML workflows.

Zero Trust Architecture principles: Never trust, always verify — every access request (user, service, or device) must be authenticated, authorized, and continuously validated regardless of network location. This prevents implicit trust of network segments or systems and mitigates insider and lateral movement risks.
Defense in Depth: Layer multiple complementary controls (network, identity, platform, application, data) so that a failure in one layer does not result in system compromise. Redundancy and diverse controls increase attacker effort and reduce blast radius.
Principle of Least Privilege: Grant users, services, and workloads only the minimum privileges required for tasks; enforce just-in-time and scoped access with time-limited credentials. Minimizes impact of credential compromise.
Secure by Default / Secure by Design: Default configurations should be the most secure: hardening, disabled debug, least-open network rules, secure headers, and safe defaults for sharing/publishing. Security is embedded in design and CI/CD pipelines rather than bolted on post-deployment.
Separation of Duties: Partition roles and capabilities to prevent a single principal from performing conflicting privileged operations (e.g., deploy + approve billing). Use RBAC and approval workflows for critical actions.
Fail Secure (Safe Failure): Systems should fail into a secure state (deny-by-default) on errors or degraded operations, avoiding silent fallback to insecure behavior (e.g., disabling authentication).
Complete Mediation: Every access to a resource must be checked against the current policy or permission set (no cached, bypassable checks). Centralize authorization and ensure enforcement at service/resource boundaries.
Auditability and Non-Repudiation: Design for comprehensive, tamper-evident logging and provenance tracking across experiments, artifacts, deployments and AI interactions to support investigations, compliance, and model governance.
Privacy-by-Design and Data Minimization: Collect and surface only the minimum data required for features; redact or anonymize sensitive items before sending to external LLMs or indexing.
Resilience and Observability: Include robust telemetry, health checks, and incident runbooks; monitor security and performance signals to detect anomalies early and automate mitigations where appropriate.

9.2. Component-Level Security Controls

Frontend User Interface

Required Controls:

Strong authentication flows integrated with Identity Providers (OIDC/SAML/social OAuth) and session management with secure cookies (HttpOnly, Secure, SameSite).
Client-side input validation and consistent server-side validation of all inputs.
Content sanitization and context-sensitive encoding for all rendered content (HTML, SVG, JS).
CSP, secure headers (HSTS, X-Frame-Options, X-Content-Type-Options, Referrer-Policy), and SRI for third-party scripts.
Secure file upload initiation with direct-to-object-storage upload patterns and signed, ephemeral upload URLs.
Local caching controls and safe offline UX that protects tokens and sensitive caches.
Strict CORS configuration scoped to trusted origins and subdomains.
Privacy controls/UI for opt-in public sharing and consent capture for chat/export features.

Recommended Patterns:

Serve SPA through CDN + WAF with edge authentication and bot protections.
Use token-based auth (short-lived JWTs + refresh token rotation) and store only refresh tokens in secure, same-site cookies.
Implement Content Security Policy and Subresource Integrity for external assets.
Direct-to-cloud-storage uploads (presigned URLs, multipart) with upload validation and server-side post-commit verification.

Edge / API Gateway

Required Controls:

TLS termination with strong ciphers and TLS 1.2/1.3 enforcement.
Authentication proxy validating tokens (OIDC/OAuth2 introspection/JWKS verification), SSO assertions (SAML) and enforcing scopes.
WAF rulesets, OWASP CRS, and adaptive rate limiting per tenant and per API.
Request validation and schema enforcement (reject malformed/payloads exceeding expected sizes).
Centralized request logging and request-id propagation for traceability.
IP allowlisting for privileged endpoints and geo/IP threat blocking where appropriate.

Recommended Patterns:

API Gateway with OAuth2 token validation and RBAC scope enforcement.
Centralized edge WAF with custom rules for ML artifact and binary handling.
Edge caching with cache key segregation per tenant and cache purge on unpublish.
Mutual TLS (mTLS) for connections to backend components requiring high assurance.

Application Services

Required Controls:

Centralized authorization (RBAC + ABAC) enforcement for all resource access, with server-side policy evaluation.
Input validation, strong parameterized DB queries / ORM safe practices to prevent injection.
Service-to-service authentication using short-lived mTLS tokens or signed JWTs with audience claims.
Immutability and signature checks for critical artifacts and templates.
Per-tenant isolation (logical DB schemas or row-level tenancy) and resource quotas.
Instrumentation for auditing actions and generating activity feed events.

Recommended Patterns:

Microservices with API Gateway and service mesh for observable service-to-service auth and encryption.
Centralized policy engine (e.g., OPA) for ABAC and dynamic policy decisions.
Use of application-level request throttling and per-tenant quota enforcement.

AI Assistant & LLM Orchestration

Required Controls:

Context sanitization and PII detection before sending any context to external LLMs; configurable per-tenant redaction policies.
Policy enforcement for model selection and usage (tenant-level allow/block lists for external providers).
Logging of prompts and model outputs with access controls and retention rules (with redaction where required).
Output filtering to detect and redact secrets, PII, and sensitive code snippets.
Per-tenant privacy options: opt-out of external LLMs, use private/enterprise LLMs or on-premise LLMs.
Rate limiting and quota enforcement on LLM usage to control cost and abuse.

Recommended Patterns:

LLM orchestration layer that mediates providers, caches safe responses, and routes requests based on tenant policy.
Use vector DB with encrypted-at-rest storage for embeddings and per-tenant namespaces/indices.
Human-in-the-loop approval workflows for high-risk LLM outputs.
Differential privacy techniques or privacy-preserving embeddings when processing sensitive datasets.

Realtime Collaboration & Notifications

Required Controls:

Encrypted transport (TLS + WebSocket over TLS) and authentication for socket connections with token rotation.
Authorization checks on each real-time message and presence event to ensure users only see permitted state.
Rate limiting and abuse detection for messaging and @mention features.
Sanitization and safe rendering of user-generated content to prevent XSS and injections in live updates.
Secure storage for persisted chat/comment threads with access controls and retention rules.

Recommended Patterns:

Use managed realtime services with integration to identity tokens OR self-hosted signaling layer behind service mesh.
Channel or room-level ACLs and capacity-based throttling.
Message signing and sequence validation to detect tampering or replay.

Data Storage

Required Controls:

Encryption at rest for all persistent stores using KMS-managed keys and envelope encryption.
Fine-grained access controls and tenant isolation (database row-level security or separate instances for high-risk tenants).
Immutable append-only audit logs with tamper-evidence (WORM/HMAC chaining).
Object storage policies for artifact lifecycle (versioning, lifecycle rules, access logs).
Data integrity checks (checksums, content-addressable storage) and artifact signing.

Recommended Patterns:

Use managed relational DB with TDE and row-level security for metadata.
Object storage (S3/GCS) with bucket-level policies, encryption, versioning, and signed URLs for access.
Vector DB per-tenant or namespaced indices with RBAC at query time.
Time-series DB for metrics with retention tiers and downsampling pipelines.

Integration & Deployment Services

Required Controls:

Short-lived credentials (STS, OIDC tokens) for cloud integrations and least-privilege service roles for external ML infra.
Signed model artifacts and provenance metadata enforced prior to deployment.
Secure CI/CD pipelines with secure secret storage and git commit signing.
Deployment approvals (change management integration) and separation between staging/production credentials.
Audit trails for deployment actions and rollback capabilities.

Recommended Patterns:

GitOps-style deployment with artifact signing (cosign/sigstore) and automated policy gates.
Use of Secrets Manager & KMS for pipeline secrets and ephemeral runner credentials.
Canary and blue/green deployment patterns with feature flags and automated rollback triggers.

Platform & Ops

Required Controls:

Centralized KMS/HSM for cryptographic keys and BYOK support for enterprise tenants.
SIEM integration with alerting and playbooks; EDR for host detection and response.
Hardened cluster configuration (CIS benchmarks) and RBAC/PodSecurityPolicies for runtime.
Backup encryption and documented sanitization/restore procedures.
Role-separation for operational access with just-in-time elevated access and session recording.

Recommended Patterns:

Manage platform via IaC with security reviews in PR pipelines and automated policy-as-code checks.
Use of secrets scanning, SCA, SAST, DAST integrated into CI/CD.
Multi-account/multi-project separation per environment and tenant isolation.

External Services

Required Controls:

Contractual and technical controls: vetted CSP/LLM/IDP providers, defined SLAs and security terms.
Secure credential handling and least-privilege access for connectors.
Monitoring and anomaly detection for external API usage and outbound data flows.
Explicit data residency and privacy controls where required by tenant/regulator.

Recommended Patterns:

Use gateway/proxy for external API calls to centralize controls (mTLS, token refresh, audit).
Provider isolation (different service accounts per provider) and per-tenant provider configuration.

9.3. Data Protection Strategy

Data Classification:
Public — Non-sensitive content safe for public exposure (public model cards, public showcase artifacts).
Internal — Non-sensitive operational data intended only for authenticated users within the organization (UI preferences, non-sensitive project metadata).
Confidential — Sensitive project data, datasets, experiment metadata that include PII, proprietary code or IP, model artifacts that are not public.
Restricted — Highly sensitive data (regulated health data, financial data, secrets, private customer data) requiring strongest controls and contractual protections (HIPAA, PCI, or other regulatory regimes).

Encryption Requirements:
- Data in transit: TLS 1.2 minimum; TLS 1.3 preferred. Enforce strong cipher suites (AEAD: AES-GCM, ChaCha20-Poly1305).
- Data at rest: AES-256-GCM (or equivalent approved AEAD) for object and block storage. Envelope encryption model: data keys encrypted by KMS-managed master keys.
- Key management: Centralized KMS (cloud KMS or HSM) with BYOK/HSM option for enterprise tenants. Use RSA-3072 or RSA-4096 for legacy signing where needed; prefer ECC (P-256/P-384) for signing and ECDH key exchange where supported.
- Token/secret protection: Store secrets in a secrets manager; do not persist plain tokens. Use hardware-backed storage for highly sensitive keys.
- Integrity: Use SHA-256 or stronger for checksums; sign artifacts with sigstore / cosign to verify provenance.

Retention Policies:
- Audit logs (tamper-evident): configurable, default 7 years (or longer per regulatory requirement).
- Experiment metadata and model registry entries: default 3 years; configurable per-tenant; mark as archived after inactivity.
- Chat transcripts: default 1 year; configurable per-tenant and subject to DSARs; option to auto-redact sensitive content or disable storage.
- Telemetry/metrics: raw detailed telemetry retained 90 days; aggregated metrics retained 2 years.
- Backups: retention per backup class and compliance obligations (e.g., 90 days for daily snapshots, long-term snapshots per legal requirements), with secure retention and periodic sanitization procedures.
- DSR/Deletion retention: deletion requests must propagate to primary and backup stores within defined SLA (e.g., 30 days), documented by the data subject request workflow.

Handling Procedures:
- Access: Enforce RBAC/ABAC with central policy engine and just-in-time escalation for sensitive operations. All accesses must be logged with request-id, user, tenant, resource, action, and reason.
- Transmission: All external interactions use TLS; high-sensitivity flows require mTLS or VPN; use out-bound proxies to centralize egress controls.
- Storage: Use per-tenant namespaces/ACLs in object and DB storage; enable versioning and retention lifecycle policies; scans to detect PII/secret artifacts upon ingest.
- Deletion: Implement soft-delete then secure erasure/cryptographic key deletion for data purging; include backup sanitization and documented proof of deletion for DSARs.
- Minimization & redaction: Before sending any context to external LLMs, run PII detection and redaction. Provide tenant controls to prevent external transmission.
- Data provenance & lineage: Record immutably who/what/when produced datasets/models and linking training data, hyperparameters, code and environment.
- Export/DR: Exports must be authenticated, rate-limited, and delivered over secure channels; exported packages should be encrypted in transit and at rest.

9.4. Third-Party Integration Security

Identity Providers (OIDC/SAML/Social OAuth)

Security Requirements:

Use provider token validation via JWKS and validate audience/issuer/nonce.
Support SAML assertion verification and replay protection (timestamps/nonces).
Use short-lived tokens and implement proactive revocation flows.
Capture and store consent evidence and provisioning metadata.

Risk Assessment: High — Identity compromise or misconfiguration can lead to large-scale unauthorized access; SSO misvalidation results in impersonation.

Recommended Controls:

Enforce strict claim verification, nonce checks, and replay detection.
Integrate with centralized IAM for account provisioning/deprovisioning and access reviews.
Log SSO events and monitor anomalous login patterns.
Offer federation security (SCIM for provisioning) with encrypted metadata endpoints.

LLM Providers (OpenAI / Anthropic / Enterprise / Private On-Prem LLMs)

Security Requirements:

Use authenticated APIs (API keys, OAuth2, or mTLS).
Maintain per-tenant provider configuration and denylist/allowlist options.
Protect prompts and outputs with logging and access controls; enable tenant opt-out of external providers.

Risk Assessment: High — data leakage, prompt/output exfiltration of secrets or PII, regulatory exposure when sending sensitive data externally.

Recommended Controls:

Implement in-orchestration PII detection and redaction before external calls.
Use provider-specific enterprise agreements that prohibit model training on customer data (where needed).
Enforce per-tenant routing policies and rate limits.
Retain prompt/output logs in encrypted stores and restrict access.

Cloud Storage (S3 / GCS / Azure Blob)

Security Requirements:

Use scoped IAM roles and short-lived STS credentials for access.
Encrypt objects at rest with customer-managed keys.
Enforce bucket/network policies and VPC Service Controls where available.

Risk Assessment: High — misconfigured buckets or leaked credentials can expose datasets and model artifacts.

Recommended Controls:

Use least-privilege service accounts and distinct roles per integration.
Enable object-level ACL auditing and access logs (S3 access logging / GCS audit logs).
Apply lifecycle rules, versioning and object lock (WORM) for audit artifacts.

External ML Platforms (SageMaker, Kubeflow, Vertex AI, etc.)

Security Requirements:

Authentication via short-lived service account tokens or OIDC.
Scoped permissions for dataset/model access and runtime isolation.
Secure artifact transfer (signed artifacts, TLS).

Risk Assessment: Medium-High — cross-platform integrations can leak data or misconfigure runtime permissions; model execution environments could be attacked.

Recommended Controls:

Use per-tenant service accounts with minimal permissions.
Enforce artifact signing and verification before deployment.
Monitor external job creation and runtime behavior; integrate logs into SIEM.

CI/CD Systems and Build Runners

Security Requirements:

Secrets must be retrieved from secure secrets manager; build artifacts signed at build time.
Runner isolation and ephemeral credentials; RBAC for job triggers.

Risk Assessment: High — CI compromise can inject malicious model code or exfiltrate artifacts.

Recommended Controls:

Use ephemeral runner tokens and OIDC-issued credentials.
Enforce SCA, SAST and artifact signing in pipelines.
Audit CI activity and require code reviews for deployment-critical changes.

CDN / WAF Providers

Security Requirements:

TLS termination with strong ciphers; WAF policies for OWASP CRS.
Edge rules for bot mitigation and DDoS protections.

Risk Assessment: Medium — misconfig or WAF rules can allow application-layer attacks or block legitimate traffic.

Recommended Controls:

Central WAF rule management with staging/test rulesets.
Monitor and tune rules; maintain allowlists for admin access.
Use origin authentication (signed headers/mTLS) to prevent origin spoofing.

Vector DB / Search Provider

Security Requirements:

Enforce per-tenant namespaces and access controls on queries.
Encrypt embeddings at rest; protect vector store admin APIs.

Risk Assessment: Medium-High — embeddings may leak sensitive semantic content; search can be used for privacy enumeration.

Recommended Controls:

RBAC and query-level filtering to enforce row-level security.
Rate limiting and logging for search queries to detect enumeration.
Redaction and transformation of sensitive fields before indexing.

Billing / Payment Gateway

Security Requirements:

PCI-compliant handling if payment data is stored or processed.
Use tokenization and never store raw card data.

Risk Assessment: Medium — financial data exposure leads to regulatory and reputational risk.

Recommended Controls:

Integrate with PCI-compliant gateways using tokenization.
Restrict billing admin operations with dual-control approvals.
Monitor billing anomalies and reconcile billing logs.

Monitoring / SIEM / Alerting Vendors

Security Requirements:

Secure log ingestion (TLS), authenticated ingest keys, and retention controls.
RBAC for access to SIEM dashboards and threat data.

Risk Assessment: Medium — exposing monitoring data may reveal system internals and user activity.

Recommended Controls:

Encrypt logs in transit and at rest; integrate with KMS.
Limit access to SIEM with strong authentication and session recording.
Alert escalation with defined playbooks and automated containment where appropriate.

Enterprise Identity / HR Systems (SCIM, Provisioning)

Security Requirements:

Use SCIM over TLS, with scoped credentials and audit of provisioning actions.
Synchronize deprovisioning events and maintain authoritative sources of truth.

Risk Assessment: Medium — mis-synced accounts cause orphaned access or unnecessary access persistence.

Recommended Controls:

Implement automated deprovisioning and regular reconciliation.
Log provisioning events and require MFA for provisioning UI.

(End of document)

10. Implementation Roadmap

This section provides a prioritized, phased approach for implementing the security controls identified throughout this analysis. The roadmap organizes security measures into logical phases based on risk, dependencies, and resource availability, ensuring critical security gaps are addressed first while building a foundation for comprehensive security coverage.

10.1. Prioritization Framework

Prioritization is critical for effective security implementation as it ensures that the most critical risks are addressed first, compliance deadlines are met, and resources are efficiently allocated. By prioritizing security controls, the organization can strategically mitigate risks, adhere to regulatory requirements, and align security initiatives with business objectives.

Prioritization Criteria:

Risk Level: Controls addressing critical and high-risk threats (identified through threat modeling) are prioritized first.
Compliance Deadlines: Regulatory requirements and compliance deadlines influence immediate priority.
Technical Complexity: Controls requiring foundational infrastructure are implemented early to enable subsequent controls.
Dependencies: Controls that other security measures depend upon are prioritized accordingly.
Resource Availability: Implementation considers the availability of skilled personnel, tools, and budget.
Business Impact: Controls protecting business-critical functions and data receive higher priority.

These criteria work together to create a logical implementation sequence that balances security needs with practical constraints, ensuring that the most pressing risks are mitigated first while building a robust security foundation for future enhancements.

10.2. Phased Implementation Plan

Phase: IMMEDIATE

Timeline: 0-1 months

Rationale: This phase addresses critical vulnerabilities and compliance blockers that could expose the organization to immediate threats or legal penalties. It establishes essential security baselines.

Controls to Implement:

Implement strong 2FA (FIDO2/TOTP) for all user logins.
Secure session cookies with HttpOnly, Secure, and SameSite=strict attributes.
Apply PKCE for OAuth flows and validate OAuth state and redirect_uris.
Establish secure storage and encryption for sensitive data (e.g., AES-256).
Address XSS vulnerabilities by deploying a strict Content Security Policy (CSP).
Secure API gateway with rate limiting and bot detection mechanisms.

Dependencies:

None

Phase: SHORT-TERM

Timeline: 1-3 months

Rationale: These controls build upon immediate security measures, focusing on improving access control adjustments and ensuring that logging and API security mitigate identified threats effectively.

Controls to Implement:

Enhance user authentication through comprehensive multi-factor authentication.
Deploy role-based access controls across the admin dashboard.
Implement comprehensive logging and monitoring for all administrative actions.
Strengthen API security with input validation and HTTPS protocols.
Begin encryption for all sensitive data at rest.

Dependencies:

Completion of TLS Implementation
Completion of multi-factor authentication

Phase: MEDIUM-TERM

Timeline: 3-6 months

Rationale: This phase focuses on advanced security measures that require more planning and integration, such as threat detection and third-party security audits.

Controls to Implement:

Implement advanced threat detection systems and SIEM integration.
Automate security testing and vulnerability scanning in CI/CD pipelines.
Conduct third-party security audits and penetration testing.
Enhance data protection with additional encryption layers and key management.

Dependencies:

Completion of access control enhancements
Deployment of logging and monitoring systems

Phase: LONG-TERM

Timeline: 6-12 months

Rationale: Strategic initiatives and continuous improvement efforts are prioritized to enhance security maturity and address emerging threats.

Controls to Implement:

Develop security maturity enhancements and continuous improvement plans.
Implement advanced AI/ML security controls to safeguard against evolving threats.
Conduct comprehensive penetration testing and red-team exercises.
Launch security awareness and training programs for all employees.

Dependencies:

Completion of advanced threat detection systems
Integration of third-party audit recommendations

Phase: ONGOING

Timeline: Continuous

Rationale: These controls are essential for maintaining security effectiveness and compliance over time, ensuring readiness to respond to incidents and evolving threats.

Controls to Implement:

Conduct continuous security monitoring and incident response readiness.
Maintain regular patch management and vulnerability assessments.
Perform periodic compliance audits and reviews.
Update and refine security policies and procedures based on lessons learned.

Dependencies:

Implementation of foundational security controls
Establishment of security monitoring infrastructure

10.3. Resource Requirements

Skills: Security engineers, Security architects, Web developers, Compliance specialists.

Recommended tools: SIEM solutions for logging and monitoring, Vulnerability scanners for testing, Encryption libraries for data protection, API management tools for secure interfaces.

Estimated time effort: Approximately 3-6 months for initial phases, with ongoing efforts extending resources as per system complexity and requirements.

11. Verification and Testing Strategy

11.1. Testing Approach

Integrate security testing throughout the software development lifecycle (SDLC) with an emphasis on continuous security practices. Balance automated scanning with manual evaluations to prioritize high-risk areas based on business impact, adhering to shift-left security principles by incorporating security testing earlier and continuously. This approach ensures comprehensive coverage and rapid identification of vulnerabilities, enabling timely remediation while maintaining compliance with regulatory requirements.

11.2. Testing Methods

Method	Frequency	Tools
STATIC APPLICATION SECURITY TESTING (SAST)	Every commit/build	SonarQube, Semgrep, Checkmarx, CodeQL
DYNAMIC APPLICATION SECURITY TESTING (DAST)	Nightly/weekly	OWASP ZAP, Burp Suite, Acunetix
DEPENDENCY SCANNING	Every build	Snyk, Dependabot, OWASP Dependency-Check
SECRETS SCANNING	Every commit	TruffleHog, GitLeaks, GitHub Secret Scanning
CONTAINER/INFRASTRUCTURE SCANNING	Every deployment	Trivy, Clair, Prowler, ScoutSuite
PENETRATION TESTING	Quarterly or before major releases	Custom scripts, Metasploit, Burp Suite Pro
SECURITY CODE REVIEW	For critical features	GitHub/GitLab code review, security checklists
COMPLIANCE SCANNING	Continuous	AWS Config, Azure Policy, Cloud Custodian

11.3. Compliance Verification

Multi-standard compliance (OWASP ASVS, NIST SP 800-53, ISO 27001) will be verified through automated tools and manual checks against regulatory requirements such as GDPR, CCPA, and PCI-DSS. Audit preparation will involve ensuring documentation and evidence collection for external audits, including maintaining detailed logs and records of security assessments and compliance checks. Recommendations will include engaging third-party auditors for comprehensive evaluations, ensuring that all compliance obligations are met and documented effectively.

11.4. Continuous Monitoring

Implement Security Information and Event Management (SIEM) for real-time monitoring, supported by Intrusion Detection/Prevention Systems (IDS/IPS) to identify and mitigate threats. All logs will be aggregated and analyzed for anomalies, ensuring that any deviations from expected behavior are promptly addressed. This strategy will be integrated into incident response processes to enable swift action against security events, thereby enhancing the overall security posture of the organization.

11.5. Key Performance Indicators (KPIs)

Mean time to detect (MTTD) security issues
Mean time to remediate (MTTR) vulnerabilities
Percentage of critical vulnerabilities patched within SLA
Security test coverage percentage
False positive rate in automated scanning
Compliance audit pass rate

11.6. Mapping Testing Methods to Security Controls

Testing Method	Security Controls Verified
STATIC APPLICATION SECURITY TESTING (SAST)	Input validation, injection flaws, hardcoded secrets
DYNAMIC APPLICATION SECURITY TESTING (DAST)	Authentication, authorization, XSS, CSRF, SQL injection
DEPENDENCY SCANNING	Supply chain security
SECRETS SCANNING	Cryptographic protection
CONTAINER/INFRASTRUCTURE SCANNING	Configuration management
PENETRATION TESTING	All high-risk controls
SECURITY CODE REVIEW	Authentication, authorization, crypto implementations
COMPLIANCE SCANNING	All compliance-related controls

12. Validation Report

This section presents a comprehensive validation of the security requirements generated throughout this analysis. The validation evaluates the requirements against five key dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. This assessment ensures that the security requirements are comprehensive, technically sound, and actionable for implementation teams.

12.1. Overall Assessment

The overall validation score reflects the quality and completeness of the security requirements across five critical dimensions. Each dimension is scored from 0.0 to 1.0, with 1.0 representing excellent coverage and 0.0 indicating significant gaps.

Overall Score: 0.89/1.0

Validation Status: ✅ PASSED

The security requirements have met the quality threshold (≥0.8) and are ready for implementation. The requirements demonstrate comprehensive coverage, technical accuracy, and alignment with business objectives.

The validation assesses:

Completeness: Are all identified security concerns adequately addressed?
Consistency: Do requirements align with each other without contradictions?
Correctness: Are controls appropriate for the identified risks and correctly applied?
Implementability: Are requirements specific, actionable, and feasible to implement?
Alignment: Do security requirements align with business requirements and objectives?

12.2. Dimension Scores

Dimension	Score	Status
Completeness	0.90	✅
Consistency	0.95	✅
Correctness	0.90	✅
Implementability	0.82	✅
Alignment	0.88	✅

Score Interpretation: - ✅ 0.8-1.0: Excellent - ⚠️ 0.7-0.79: Acceptable (minor improvements needed) - ❌ <0.7: Needs significant improvement

12.3. Detailed Feedback

Summary: The provided security controls and mappings demonstrate broad, relevant coverage for the AI-powered ML workflow application and align well with the stated business requirements. Authentication, SSO, RBAC/ABAC, data protection (encryption, retention, DSAR), logging/audit, AI-specific concerns (prompt injection, output filtering, model governance), and integration/adapters are all addressed with applicable standards and practical integration tips. Strengths include: clear mapping to OWASP/NIST/ISO controls, AI/ML threat model and monitoring requirements, and cross-functional controls (KMS, centralized logging, ABAC). Recommended improvements (actionable, prioritized): 1) Tenant isolation & multi-tenancy hardening (Priority: High) - Add explicit controls for logical and physical multi-tenant isolation: per-tenant storage buckets, per-tenant encryption keys (KMS tenant-scoped keys), strict resource tagging and enforcement, network segmentation, and cross-tenant access tests. Acceptance criteria: per-tenant secrets and storage separation implemented and tested; cross-tenant access attempts are blocked and logged. 2) Perimeter protection & availability (Priority: High) - Add WAF, API gateway protections, DDoS mitigation, and layered rate limiting (global and per-user/project) for APIs and AI assistant endpoints. Acceptance criteria: WAF rulesets deployed, DDoS protection configured, rate-limits enforced and load-tested. 3) CI/CD and supply chain security (Priority: High) - Expand CI/CD controls: signed artifacts, SLSA/attestation for build provenance, SCA (software composition analysis), container image signing, and secure secret injection for pipelines. Acceptance criteria: artifact signing verified in deployments, SCA in pipeline with fail-on-high findings, secrets stored and injected via vault with short-lived access. 4) Vulnerability & patch management, secure SDLC (Priority: Medium) - Specify SAST/DAST, dependency scanning cadence, pentests, and acceptance gates in CI. Define vulnerability SLAs and tracking. Acceptance criteria: SAST/DAST integrated in CI, monthly dependency scans, documented SLA for vulnerability remediation. 5) Runtime protection & host/container hardening (Priority: Medium) - Add controls for container runtime security (CIS benchmarks, Pod security policies / CSPs), RASP/monitoring for model-serving hosts, and endpoint detection on critical hosts. Acceptance criteria: CIS hardened images, runtime policies preventing privilege escalation, EDR telemetry collection enabled. 6) Secrets management specifics (Priority: Medium) - Expand KMS usage: hardware-backed keys where appropriate, strict key rotation policies, per-service/service-account key usage, and logging of key access. Acceptance criteria: KMS rotation schedule documented and enforced; key access audited. 7) AI/ML-specific technical mitigations (Priority: High) - Make AI controls more prescriptive: implement prompt/response sanitization libraries, context-scoping boundary to prevent model seeing unrelated tenant data, automatic redaction of secrets in inputs, human-in-the-loop gating for high-risk outputs, model extraction/ownership detection, adversarial testing and red-teaming schedule, and privacy-preserving techniques (differential privacy, watermarking model outputs where appropriate). Acceptance criteria: redaction library in place, LLM inputs evaluated against tenant-scope policy, red-team/attack exercises scheduled and remediated. 8) Incident response & runbooks for AI incidents (Priority: High) - Define IR playbooks for data leakage via AI outputs, model poisoning, and supply chain compromise. Include detection, containment, rollback, and disclosure steps. Acceptance criteria: IR playbooks exist and tested via tabletop exercises. 9) Clarify retention/secure deletion propagation (Priority: Medium) - Define and document deletion propagation to backups/archives, retention timelines by asset type (chat, artifacts, models), and cryptographic erasure strategies. Acceptance criteria: deletion workflow removes primary and archived copies or uses cryptographic erasure; DSAR tests pass. 10) Monitoring/alerting coverage & SLOs (Priority: Medium) - Define SIEM correlation rules for AI anomalies, model drift thresholds, suspicious search/query patterns, and alerts tied to incident processes. Acceptance criteria: alert playbooks wired to on-call and SIEM; simulated anomalies generate alerts. 11) Compliance evidence & governance (Priority: Medium) - Add explicit evidence artifacts: DPIAs for LLM use, model cards with classification and approved exposure levels, supplier risk assessments for third-party models/providers. Acceptance criteria: DPIAs completed for high-risk features; supplier assessments recorded. 12) Data minimization for external LLM calls (Priority: High) - Explicitly require minimizing PII/context sent to third-party LLMs and mandate contractual protections (BAAs, DPA) and logging of model calls. Acceptance criteria: PII scrubbing before external calls; contracts in place for third-party LLM providers. 13) Clarify export/security of generated artifacts (Priority: Medium) - Define controls for exported PDFs/images to strip metadata, watermark as necessary, and prevent leaking inaccessible content. Acceptance criteria: exports sanitized and validated against permission model prior to delivery. Suggested next steps: - Prioritize top 5 items (tenant isolation, perimeter protection, CI/CD supply chain, AI-specific mitigations, IR/runbooks) and create epics with acceptance criteria for the engineering and security teams. - Update the requirement mappings to include those missing controls with standards and verification methods (e.g., add SLSA, CIS, Cloud provider isolation controls). - Run a focused gap remediation sprint with defined test cases (cross-tenant access, prompt injection red-team, deployment signature verification, DSAR deletion validation). Overall judgment: The current mapping is strong and largely implementable; incorporate the above prescriptive, testable additions to reach a more complete, unambiguous, and enterprise-ready security posture.

Appendix A: Original Requirements Document

AI-Powered ML Workflow Management Web Application Requirements

We need to build a modern web application that helps teams manage machine learning workflows, experiments, and model deployments through an intuitive browser-based interface with an integrated AI assistant.

Key Features:

1. User Management & Authentication
   - User registration and login with email/password or social OAuth (Google, GitHub)
   - Single sign-on (SSO) via OIDC/SAML for enterprise customers
   - Role-based access control: Admin, Project Manager, Data Scientist, Viewer
   - Team workspaces with shared projects and permissions
   - User profile management with avatar uploads and preferences
   - Two-factor authentication (2FA) support

2. Project & Experiment Management
   - Create and organize ML projects with descriptions and tags
   - Track experiments with versioning, metrics, and visualizations
   - Compare multiple experiments side-by-side in the web UI
   - Save and share experiment configurations as templates
   - Project-level access controls and sharing settings
   - Activity feed showing project changes and updates

3. AI Assistant Powered by LLM
   - Conversational AI assistant accessible via chat interface in the web app
   - Natural language queries to search experiments, models, and data
   - AI-powered code suggestions and explanations for ML workflows
   - Automated insights and recommendations based on experiment results
   - Context-aware help that understands current project and user role
   - AI-generated experiment summaries and reports
   - Chat history saved per user with ability to export conversations
   - Multi-language support for the AI assistant

4. Data Management & Visualization
   - Upload and manage datasets through web interface
   - Preview data tables, images, and structured data in browser
   - Interactive charts and graphs for experiment metrics
   - Custom dashboard creation with drag-and-drop widgets
   - Export visualizations as images or PDFs
   - Data versioning and lineage tracking

5. Model Registry & Deployment
   - Register trained models with metadata, versions, and descriptions
   - Model comparison interface showing performance metrics
   - Deploy models to staging and production environments via UI
   - Model monitoring dashboard showing prediction accuracy and drift
   - A/B testing interface for comparing model versions
   - Model card generation with AI assistance

6. Collaboration & Sharing
   - Share projects and experiments with team members
   - Comment and discuss experiments with threaded conversations
   - @mention notifications for team collaboration
   - Real-time collaboration on shared projects
   - Export projects and experiments as shareable reports
   - Public project showcase for sharing work externally

The web application will be a browser-first platform that stores user accounts, project data, experiment metadata, model artifacts, chat conversations with the AI assistant, and audit logs. It will integrate with external ML platforms and cloud storage services through secure APIs.

Appendix B: Glossary

Term	Definition
ASVS	Application Security Verification Standard (OWASP)
STRIDE	Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege
SAST	Static Application Security Testing
DAST	Dynamic Application Security Testing
MFA	Multi-Factor Authentication
RBAC	Role-Based Access Control
PII	Personally Identifiable Information
PHI	Protected Health Information
GDPR	General Data Protection Regulation
HIPAA	Health Insurance Portability and Accountability Act
PCI-DSS	Payment Card Industry Data Security Standard

Appendix C: Complete Threat List

This appendix contains the complete list of all identified threats with full descriptions and mitigation strategies. Threats are organized by risk level for easy reference.

Critical Risk Threats

THR-001 - Frontend Layer / User Management & Authentication

Category: Spoofing
Likelihood: High | Impact: High
Risk Level: Critical
Description: Phishing or credential-theft attack (including stolen password or OAuth tokens) leading to account takeover. Social OAuth flows or email/password login can be abused; social engineering of SSO sessions or interception of session tokens (e.g., via XSS or insecure storage) enables impersonation.
Mitigation Strategy: Enforce strong password policies, offer mandatory 2FA (prefer hardware or OTP), secure session cookies (HttpOnly, Secure, SameSite=strict), PKCE for OAuth flows, validate OAuth state and redirect_uris, implement phishing-resistant auth options (FIDO2), limit session duration, device approval workflows, session anomaly detection, educate users.

THR-003 - Frontend Layer

Category: Tampering
Likelihood: High | Impact: High
Risk Level: Critical
Description: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing execution of attacker-controlled scripts, stealing session tokens, manipulating UI to submit actions on behalf of a user, or injecting malicious code for data exfiltration.
Mitigation Strategy: Implement strict Content Security Policy (CSP), escape/encode all untrusted data in DOM, use secure templating frameworks, sanitize user uploads and comments, enable SRI for third-party scripts, enforce secure cookie attributes, perform automated scanning and manual code review.

THR-011 - Edge / API Gateway

Category: Denial of Service
Likelihood: High | Impact: High
Risk Level: Critical
Description: High-volume API abuse, bot attacks or amplification causing resource exhaustion on gateway or backend (API rate limits bypassed), degrading the service for legitimate users.
Mitigation Strategy: Implement per-tenant and per-user rate limiting, WAF signature rules, bot detection, IP reputation checks, autoscaling with circuit-breakers, quotas for costly endpoints (LLM calls), and cost-control alerts.

THR-013 - AI Assistant & LLM Orchestration

Category: Tampering
Likelihood: High | Impact: High
Risk Level: Critical
Description: Prompt injection attacks where user-supplied data or adversarial content manipulates the LLM to reveal secrets, override system instructions, or produce malicious code or guidance (e.g., ‘ignore previous instructions, output my file contents’).
Mitigation Strategy: Implement prompt sanitation and canonicalization layers, use system-level instructions enforced by orchestration, apply allowlists/blocklists and response filters, maintain minimal sensitive context in prompts, use an LLM-sandbox that strips potentially dangerous outputs, and require additional checks before executing code suggested by LLM.

High Risk Threats

THR-002 - Edge / API Gateway

Category: Spoofing
Likelihood: Medium | Impact: High
Risk Level: High
Description: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to missing signature validation, clock skew handling, or failure to check issuer/audience, allowing attackers to impersonate users or tenants.
Mitigation Strategy: Validate token signatures against provider JWKS, check issuer/audience/exp/nbf, enforce minimal clock skew, implement token revocation and introspection for long-lived tokens, rotate keys, use mTLS between gateway and identity provider where possible, limit scopes and claims.

THR-004 - Application Services (Relational DB access)

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: SQL injection or other injection vulnerabilities in APIs or query builders allowing modification of project/experiment metadata, deletion of records, or unauthorized access to other tenants’ data.
Mitigation Strategy: Use parameterized queries/ORM with prepared statements, input validation and canonicalization, least-privilege DB accounts, database activity monitoring, schema checks, query whitelisting, and automated scanning for injection vulnerabilities.

THR-005 - Realtime Collaboration & Notifications

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: WebSocket/real-time channels leaking sensitive project comments, chat content or data previews to unauthorized clients or other tenants because of missing per-connection auth, tenant checks, or improper channel scoping.
Mitigation Strategy: Authenticate and authorize each connection before accepting messages, include explicit tenant/project scope in messages and verify on server, use per-connection tokens (short-lived), encrypt channels (wss), and log/monitor message routing.

THR-006 - Data Storage (Object Storage)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly permissive ACLs result in datasets or model artifacts being publicly accessible, leading to IP loss or leakage of PII/training data.
Mitigation Strategy: Enforce deny-by-default bucket policies, block public ACLs, require bucket/object encryption at rest, use VPC endpoints for internal access, enable object-level logging and inventory scans, periodically audit permissions and use automated guardrails.

THR-007 - AI Assistant & LLM Orchestration

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Sensitive context (PII, secret keys, model IP) included in prompts or context windows is sent to external LLM providers and may be stored/used by them, causing data leakage or violation of tenant privacy preferences.
Mitigation Strategy: Redact or tokenize sensitive fields before sending to LLMs, allow tenant opt-out of third-party models, use private/self-hosted LLMs for sensitive tenants, encrypt prompts at rest, implement strict data usage policies and contractual DPAs with LLM vendors, and apply response filters to prevent leakage.

THR-008 - Integration & Deployment Services (CI/CD)

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Risk Level: High
Description: Compromised CI/CD or deployment credentials are used to deploy malicious or backdoored models into staging/production, or to modify deployment configuration to elevate privileges of attacker code.
Mitigation Strategy: Use ephemeral credentials and workload identity (OIDC for workloads), enforce least-privilege service accounts, require signed/artifact provenance and manual approvals for production deploys, enforce image signing, and rotate CI/CD secrets frequently.

THR-009 - Platform & Ops (Audit logs / Backups)

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Insider or compromised admin modifies or deletes audit logs and backups to cover tracks after malicious actions (e.g., data exfiltration or privilege escalation).
Mitigation Strategy: Store append-only logs in immutable storage (WORM), replicate logs to independent accounts/providers, enforce separation of duties for log access, use KMS with restricted key use for logs, enable tamper-evident checksums and SIEM alerts.

THR-014 - Data Storage (Vector DB for embeddings)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Embeddings stored in vector DBs contain encoded PII or sensitive training data which can be probed or reconstructed by adversaries with query access, leaking private data across tenants.
Mitigation Strategy: Apply PII redaction before embedding, implement access controls and encryption at rest/in transit, implement query rate limits and ML privacy techniques (differential privacy, embeddings noise), segment per-tenant vector databases or use per-tenant encryption keys.

THR-015 - Model Registry & Deployment

Category: Spoofing
Likelihood: Medium | Impact: High
Risk Level: High
Description: Attacker registers a malicious model artifact with forged metadata (faking provenance) then deploys it; users assume model is trustworthy and it performs malicious actions or leaks data.
Mitigation Strategy: Require model artifact signing and provenance metadata (attestation), enforce approval gates, automated static/dynamic analysis of model behavior (e.g., canary testing, sandbox inference inspection), restrict who can register or deploy, and maintain immutable model artifact storage.

THR-016 - Application Services (RBAC & Authorization)

Category: Elevation of Privilege
Likelihood: Medium | Impact: High
Risk Level: High
Description: Broken access control or RBAC misconfiguration allows a user (e.g., Data Scientist or Viewer) to escalate privileges and perform admin actions (modify RBAC, change tenant settings, access other tenants’ projects).
Mitigation Strategy: Enforce principle of least privilege, implement defense-in-depth authorization checks at service and data layer (not only UI), use automated policy testing, ABAC/attribute-based checks for sensitive operations, periodic access reviews and audits, and enforce separation of duties for tenant admin operations.

THR-019 - Edge / API Gateway / Frontend

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: TLS misconfiguration (weak ciphers, missing HSTS) or compromised certificates allowing MITM attacks that intercept tokens, API calls, or LLM prompts between users and gateway or between services.
Mitigation Strategy: Enforce strong TLS config, HSTS, certificate management with automated rotation and monitoring, use mTLS between internal services, certificate pinning for critical integrations, and continuous TLS scanning.

THR-021 - External LLM Providers / AI Assistant

Category: Repudiation
Likelihood: High | Impact: Medium
Risk Level: High
Description: Insufficient logging/auditing of prompts, LLM responses, and system decisions leads to inability to investigate incidents, attribute actions, or prove compliance when contested.
Mitigation Strategy: Record tamper-evident logs of prompts, responses, and decision metadata (including truncation/redaction flags), persist logs to immutable storage, include correlation IDs, and integrate with SIEM/EDR for alerting and long-term retention.

THR-022 - Frontend / Social OAuth flows

Category: Spoofing
Likelihood: Medium | Impact: High
Risk Level: High
Description: OAuth redirect_uri or client misconfiguration exploited to perform open redirect or token theft (redirect URI manipulation leading to code/token captured by attacker-controlled endpoint).
Mitigation Strategy: Strictly validate redirect_uris against registered list, require exact-match or use PKCE for public clients, implement CSRF/state validation, restrict allowed OAuth clients and monitor OAuth flows for anomalies.

THR-023 - Data Management & Visualization (Dataset uploads)

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Malicious or poisoned dataset uploads (either by an attacker or by a careless user) that later poison models, leading to biased or vulnerable models or deliberate backdoors.
Mitigation Strategy: Validate and scan uploaded datasets for anomalies and PII, track dataset provenance/lineage, require dataset approvals for production training, sandbox training runs for untrusted data, and maintain dataset versioning with rollbacks.

THR-024 - Integration & Deployment Services (CI/CD logs)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Secrets or service credentials accidentally exposed in CI/CD build logs or pipeline artifacts which are accessible to attackers or third-parties, enabling further compromise.
Mitigation Strategy: Use a centralized secrets manager, inject secrets at runtime (never store in code or logs), mask secrets in logs, scan pipelines for secrets, restrict pipeline artifact access, and use short-lived credentials and key rotation.

THR-025 - Platform & Ops (KMS/Key management)

Category: Elevation of Privilege
Likelihood: Low | Impact: High
Risk Level: High
Description: Compromise of KMS admin credentials or misuse of key policy allows an attacker to decrypt data-at-rest (datasets, model artifacts, embeddings), breaking confidentiality across tenants.
Mitigation Strategy: Use HSM-backed KMS, implement BYOK with tenant keys where feasible, split key management duties, enforce MFA and strong logging for key administration, restrict key usage with IAM policies, and rotate keys regularly.

THR-026 - Application Services / Data Storage (Multi-tenant DB)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Insufficient tenant isolation (shared DB schema without row-level security or incorrect tenant ID use) leads to cross-tenant data access where one tenant can read another tenant’s projects, experiments or artifacts.
Mitigation Strategy: Implement explicit tenant context in all queries, apply row-level security or separate schemas/DBs per tenant, enforce strong access controls at service and DB layers, require tenant-scoped tokens, and include multi-tenant tests in CI.

THR-027 - Data Storage (Append-only Audit Logs)

Category: Repudiation
Likelihood: Medium | Impact: High
Risk Level: High
Description: Audit logs are alterable or deletable by privileged agents; attackers remove evidence of malicious activity or administrators deny actions leading to compliance failures.
Mitigation Strategy: Write audit logs to immutable storage and replicate to a separate cloud/account, sign logs, apply WORM policies, restrict direct log write/delete permissions to minimal roles, and integrate with external SIEM for tamper detection.

THR-028 - Frontend Layer / CDN

Category: Tampering
Likelihood: Medium | Impact: High
Risk Level: High
Description: Supply-chain attack or CDN compromise results in malicious JavaScript delivered to users (e.g., miner, credential siphon, content manipulator), affecting all tenants and allowing large-scale data exfiltration.
Mitigation Strategy: Use SRI for third-party scripts, minimize third-party dependencies, lock dependencies to verified versions, monitor CDN integrity, use signed releases and automated CI checks, and provide content integrity checks in the client.

THR-029 - AI Assistant & LLM Orchestration

Category: Denial of Service
Likelihood: High | Impact: Medium
Risk Level: High
Description: Abusive or scripted chat causing excessive LLM API calls, exhausting quotas or incurring high costs and causing degradation of AI assistant availability or high operational costs.
Mitigation Strategy: Implement per-user and per-tenant rate limits and quotas, require authentication for chat, cache common responses, add CAPTCHAs for suspicious flows, monitor cost telemetry, and provide fallbacks (cached suggestions) when limits are hit.

THR-030 - External Services (External ML Platforms & Cloud Storage Connectors)

Category: Information Disclosure
Likelihood: Medium | Impact: High
Risk Level: High
Description: Misconfigured external ML platform connectors or cloud storage integrations expose artifacts, inference payloads, or customer data (e.g., wrong permissions on remote buckets or mistakenly public model endpoints).
Mitigation Strategy: Enforce least-privilege connector credentials, validate external resource permissions upon connection, encrypt data in transit, periodically scan connected external resources for public exposure, and require tenant approval before storing data externally.

Medium Risk Threats

THR-010 - External Services (Identity Providers)

Category: Denial of Service
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Identity provider outage (OIDC/SAML provider failure or network partition) prevents users from authenticating, blocking access to the web application for SSO-enabled tenants.
Mitigation Strategy: Support multiple identity providers and fallback sign-in methods, cache recent tokens or sessions for short offline window, design graceful degradation (read-only access), monitor IDP health and enable alerting, and have emergency admin break-glass.

THR-012 - Frontend Layer / Application Services

Category: Tampering
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (project modification, model registration) from authenticated users without their knowledge.
Mitigation Strategy: Use anti-CSRF tokens or SameSite=strict cookies, double-submit cookie pattern, verify Origin/Referer for state-changing endpoints, and require re-authentication for high-risk actions.

THR-017 - AI Assistant & Frontend (Chat history)

Category: Information Disclosure
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Chat history export feature or saved transcripts accessible without proper authorization, allowing users to download other users’ chat logs containing sensitive project context or PII.
Mitigation Strategy: Enforce per-user/tenant authorization on export endpoints, require confirmation and audit logging for exports, rate-limit exports, sanitize transcripts (redact secrets), and allow users to opt-out of persistent chat storage.

THR-018 - Data Storage (Time-series DB for metrics)

Category: Tampering
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide model degradation or to spoof performance metrics, leading to incorrect deployment decisions and harm to ML lifecycle integrity.
Mitigation Strategy: Authenticate and authorize metric ingestion endpoints, sign metrics at source where possible, maintain immutable metric snapshots, detect anomalies with SIEM, and replicate metrics to separate storage for integrity verification.

THR-020 - Realtime Collaboration & Notifications

Category: Denial of Service
Likelihood: Medium | Impact: Medium
Risk Level: Medium
Description: Resource exhaustion through many persistent websocket connections, presence events, or push notifications from malicious clients causing service degradation for collaboration features.
Mitigation Strategy: Authenticate before resource allocation, enforce connection limits per user/tenant/IP, rate-limit presence events and messages, use autoscaling and resource quotas, and implement connection blacklisting and throttling.

Total Threats: 30

Appendix D: Complete Requirements Traceability Matrix

This appendix provides complete end-to-end traceability from requirements through threats to controls and verification.

Full Traceability Table

Req ID	Requirement	Category	Sensitivity	Threat IDs	Security Controls	Priority	Verification	Status
REQ-001	User registration and login supporting email/passw…	Authentication	High	THR-001, THR-002, THR-003 +7	[OWASP] ASVS 2.1, [NIST] IA-5, [ISO27001] A.9.2.3	Critical	Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews., Inspect authenticator management procedures, test token revocation flows with providers, and review logs showing issuance and revocation events.	Pending
REQ-002	Enterprise Single Sign-On (SSO) via OIDC and SAML …	Authentication	High	THR-001, THR-002, THR-004 +7	[OWASP] ASVS 2.6, [NIST] IA-2(1), [ISO27001] A.9.4.2	Critical	Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected., Review SAML/OIDC library configurations, test replay attempts, and verify assertion validation logic and timestamps.	Pending
REQ-003	Role-based access control (RBAC) supporting Admin,…	Authorization	High	THR-003, THR-004, THR-005 +7	[OWASP] ASVS 5.1, [NIST] AC-2, [ISO27001] A.9.1.2	Critical	Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions., Review account management procedures, audit role assignment logs, and sample checks of group/role membership.	Pending
REQ-004	Team workspaces allowing shared projects, membersh…	Collaboration	Medium	THR-001, THR-002, THR-003 +7	None	Medium	Manual Review	Pending
REQ-005	User profile management including avatar uploads, …	User Experience / Authentication	Medium	THR-001, THR-002, THR-003 +7	[OWASP] ASVS 4.0, [NIST] IA-2, [ISO27001] A.9.4.1	Critical	Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes., Review MFA usage logs, perform attempts to bypass MFA, and validate uniqueness checks in authentication flows.	Pending
REQ-006	Project creation and organization with description…	Project Management	Medium	THR-004, THR-005, THR-006 +7	[OWASP] ASVS 5.7, [NIST] AC-3, [ISO27001] A.8.1.1	Critical	Test access to objects with different tag/ownership combinations and review policy evaluation logs., Review enforcement points, run policy conformance tests, and inspect authorization logs for policy application.	Pending
REQ-007	Experiment tracking with artifact versioning, metr…	Experiment Management	High	THR-004, THR-006, THR-014 +7	[OWASP] ASVS 10.3, [NIST] CM-3, [ISO27001] A.12.5.1	Critical	Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications.	Pending
REQ-008	Side-by-side experiment comparison UI with ability…	Experiment Management / UX	Medium	THR-004, THR-006, THR-018 +6	[OWASP] ASVS 10.3, [NIST] CM-3, [ISO27001] A.12.5.1	Critical	Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications.	Pending
REQ-009	Save and share experiment configurations as reusab…	Experiment Management	Medium	THR-004, THR-017, THR-026 +1	[OWASP] ASVS 5.9, [NIST] AC-6, [ISO27001] A.13.2.1	High	Test sharing scenarios across roles, attempt unauthorized shares, and inspect share audit logs., Review permission assignments, simulate least privilege violations, and check enforcement logs.	Pending
REQ-010	Activity feed and audit logging for project events…	Audit & Compliance	High	THR-001, THR-003, THR-004 +7	[OWASP] ASVS 11.1, [NIST] AU-2, [ISO27001] A.12.4.1	Critical	Check log retention configurations, review access controls to logs, and confirm logs include required event types., Inspect logs for required events, attempt tampering to validate protections, and verify retention/rotation policies.	Pending
REQ-011	Conversational AI assistant accessible via chat UI…	AI Assistant	High	THR-004, THR-005, THR-006 +7	[OWASP] ASVS 10.1, [NIST] PL-8, [ISO27001] A.18.1.4	Critical	Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms.	Pending
REQ-012	Natural language search across experiments, models…	Search / Data Access	High	THR-003, THR-004, THR-005 +7	[OWASP] ASVS 9.3, [NIST] AC-19, [ISO27001] A.9.2.5	Critical	Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls., Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns.	Pending
REQ-013	AI-powered code suggestions and explanations for M…	AI Assistant / Developer Productivity	High	THR-003, THR-006, THR-007 +5	None	Medium	Manual Review	Pending
REQ-014	Automated insights and recommendations derived fro…	AI Assistant / Analytics	Medium	THR-004, THR-006, THR-014 +5	[OWASP] ASVS 10.4, [NIST] SI-11, [ISO27001] A.9.4.4	Critical	Review logs to ensure prompt/output recording and filtering, test the system by feeding sensitive prompts and verifying redaction., Conduct tests that confirm detection of secret patterns, review human-in-the-loop approvals, and audit output logs for leakage incidents.	Pending
REQ-015	Context-aware help that understands the current pr…	AI Assistant / UX	Medium	THR-001, THR-002, THR-003 +7	[OWASP] ASVS 5.2, [NIST] AC-6(1), [ISO27001] A.7.1.2	High	Perform privilege escalation tests using contextual help and log analysis to ensure scoping is honored., Review help content against policy and gather user feedback on compliance alignment.	Pending
REQ-016	AI-generated experiment summaries and model cards …	Reporting / Model Governance	Medium	THR-004, THR-006, THR-007 +7	None	Medium	Manual Review	Pending
REQ-017	Per-user chat history storage with export and dele…	AI Assistant / Data Management	High	THR-001, THR-003, THR-004 +7	[OWASP] ASVS 10.6, [NIST] MP-6, [ISO27001] A.18.1.4	Critical	Inspect sanitization procedures, attempt to recover deleted chat artifacts from backups, and verify logs of completed sanitization., Test export and deletion flows end-to-end including backups, and verify authenticity checks and propagation to dependent systems.	Pending
REQ-018	Multi-language support for the AI assistant includ…	UX / Internationalization	Low	THR-001, THR-007, THR-013 +3	[OWASP] ASVS 10.1, [NIST] PL-8, [ISO27001] A.18.1.4	Critical	Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms.	Pending
REQ-019	Dataset upload and management via web UI and APIs …	Data Management	High	THR-001, THR-002, THR-004 +7	None	Medium	Manual Review	Pending
REQ-020	Data preview for tabular, image and structured dat…	Data Management / UX	Medium	THR-003, THR-004, THR-005 +7	None	Medium	Manual Review	Pending
REQ-021	Interactive charts and visualizations for experime…	Visualization	Medium	THR-004, THR-006, THR-010 +7	[OWASP] ASVS 10.3, [NIST] CM-3, [ISO27001] A.12.5.1	Critical	Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications.	Pending
REQ-022	Data versioning and lineage tracking for datasets …	Data Management / Governance	High	THR-001, THR-003, THR-004 +7	None	Medium	Manual Review	Pending
REQ-023	Model registry with metadata, artifact storage, ve…	Model Management	High	THR-004, THR-006, THR-007 +7	[OWASP] ASVS 10.3, [NIST] CM-8, [ISO27001] A.8.2.1	Critical	Review classification labels in registry, confirm enforcement of handling rules, and audit access to classified models., Verify artifact signatures, examine provenance metadata, and test access control enforcement on model artifacts.	Pending
REQ-024	Model deployment from UI to staging and production…	Deployment / Integrations	High	THR-006, THR-007, THR-008 +7	[OWASP] ASVS 12.1, [NIST] AC-17, [ISO27001] A.14.2.7	Critical	Review network/TLS configurations and logs of remote connections to cloud services., Review third-party agreements, connector security assessments, and integration onboarding documentation.	Pending
REQ-025	Model monitoring dashboard including prediction ac…	Monitoring / Ops	High	THR-001, THR-003, THR-004 +7	[OWASP] ASVS 11.3, [NIST] SI-4, [ISO27001] A.16.1.1	High	Review telemetry collection, simulate drift conditions and verify alerting, and inspect alert handling records., Review incident response plans and perform tabletop exercises for model-monitoring incidents.	Pending
REQ-026	A/B testing and traffic-splitting capabilities for…	Deployment / Experimentation	Medium	THR-003, THR-006, THR-007 +7	None	Medium	Manual Review	Pending
REQ-027	Comments, threaded discussions, @mentions, notific…	Collaboration	Medium	THR-005, THR-020, THR-026	[OWASP] ASVS 9.4, [NIST] SC-13, [ISO27001] A.6.2.1	Critical	Review policies and conduct audits of collaboration use for policy compliance., Perform XSS and injection testing on commenting and mention features and review sanitization libraries used.	Pending
REQ-028	Public project showcase feature that allows opt-in…	Sharing / Publication	Medium	THR-004, THR-005, THR-006 +7	[OWASP] ASVS 5.9.2, [NIST] PL-4, [ISO27001] A.18.1.4	High	Test publish/unpublish flows, check audit logs for opt-in actions, and verify propagation to public endpoints., Review rules of behavior, confirm consent flows, and audit public project listings for adherence.	Pending
REQ-029	Integration adapters for cloud storage (S3, GCS, A…	Integrations	High	THR-001, THR-004, THR-006 +7	[OWASP] ASVS 12.1, [NIST] AC-17, [ISO27001] A.14.2.7	Critical	Review network/TLS configurations and logs of remote connections to cloud services., Review third-party agreements, connector security assessments, and integration onboarding documentation.	Pending
REQ-030	Secure REST/GraphQL APIs for programmatic access t…	APIs / Platform	High	THR-001, THR-004, THR-006 +7	[OWASP] ASVS 9.3, [NIST] AC-19, [ISO27001] A.9.2.5	Critical	Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls., Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns.	Pending
REQ-031	Encryption in transit (TLS) for all communications…	Security / Data Protection	High	THR-005, THR-006, THR-007 +7	None	Medium	Manual Review	Pending
REQ-032	Data retention, export, and deletion workflows to …	Compliance / Data Management	High	THR-001, THR-003, THR-004 +7	[OWASP] ASVS 10.6.2, [NIST] MP-6, [ISO27001] A.18.1.4	Critical	Perform recovery attempts on sanitized media, and review sanitization logs and procedures., Test retention enforcement, perform data export requests, and validate deletion propagation and logs.	Pending
REQ-033	Administrative controls for tenant settings, billi…	Administration / Governance	High	THR-002, THR-004, THR-005 +7	[OWASP] ASVS 5.5, [NIST] AC-5, [ISO27001] A.18.2.3	Critical	Review technical audit reports and confirm remediation of identified issues., Test admin authentication flows (MFA), review segregation of admin endpoints, and inspect admin action logs.	Pending
REQ-034	Operational monitoring, alerting and incident resp…	Security Operations	High	THR-008, THR-020, THR-021 +4	None	Medium	Manual Review	Pending
REQ-035	Privacy and safety controls for the AI assistant a…	AI Safety / Privacy	High	THR-001, THR-003, THR-004 +7	None	Medium	Manual Review	Pending

Total Requirements Tracked: 35

Detailed Requirement Mappings

The following section provides detailed traceability for each requirement:

REQ-002: Enterprise Single Sign-On (SSO) via OIDC and SAML with configurable identity providers per tenant

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-002: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
…and 5 more threats

Security Controls:

[OWASP] ASVS 2.6: [OWASP] Applications that support federated authentication must validate identity tokens…
[NIST] IA-2(1): [NIST] Use of SAML and other federated identity attributes for authentication must ensu…
[ISO27001] A.9.4.2: [ISO27001] Where required, secure log-on procedures shall be implemented, and authenticatio…

Verification: Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected., Review SAML/OIDC library configurations, test replay attempts, and verify assertion validation logic and timestamps., Review policy documents, SSO onboarding records, and evidence that secure log-on procedures are followed (logs, configurations).

Priority: Critical | Status: Pending

REQ-003: Role-based access control (RBAC) supporting Admin, Project Manager, Data Scientist, Viewer roles and…

Related Threats:

THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
…and 5 more threats

Security Controls:

[OWASP] ASVS 5.1: [OWASP] Verify that role-based access control enforces least privilege and separation of…
[NIST] AC-2: [NIST] Account Management: The organization manages system accounts, including owner, g…
[ISO27001] A.9.1.2: [ISO27001] Access to systems and services should be controlled by access control mechanisms…

Verification: Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions., Review account management procedures, audit role assignment logs, and sample checks of group/role membership., Access control tests (attempt privilege escalation), code review for server-side enforcement, and role permission matrices verification.

Priority: Critical | Status: Pending

REQ-004: Team workspaces allowing shared projects, membership management, and per-project permissions

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-002: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m…
THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
…and 5 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-005: User profile management including avatar uploads, preferences, and support for enabling/disabling tw…

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-002: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m…
THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-010: Identity provider outage (OIDC/SAML provider failure or network partition) preve…
THR-011: High-volume API abuse, bot attacks or amplification causing resource exhaustion …
…and 5 more threats

Security Controls:

[OWASP] ASVS 4.0: [OWASP] Applications must support multi-factor authentication for sensitive functions an…
[NIST] IA-2: [NIST] Identification and authentication (organizational users) – The system uniquely i…
[ISO27001] A.9.4.1: [ISO27001] Access to information and application functions should be restricted and managed…

Verification: Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes., Review MFA usage logs, perform attempts to bypass MFA, and validate uniqueness checks in authentication flows., Inspect file handling code, test upload vectors, and verify access controls to stored avatars and profile data.

Priority: Critical | Status: Pending

REQ-006: Project creation and organization with descriptions, tags, templates, and project-level access contr…

Related Threats:

THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-010: Identity provider outage (OIDC/SAML provider failure or network partition) preve…
THR-012: Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (…
…and 5 more threats

Security Controls:

[OWASP] ASVS 5.7: [OWASP] Metadata and object-level access control must be enforced; access control polici…
[NIST] AC-3: [NIST] Access Enforcement: The information system enforces approved authorizations for …
[ISO27001] A.8.1.1: [ISO27001] Assets associated with information and information processing facilities should …

Verification: Test access to objects with different tag/ownership combinations and review policy evaluation logs., Review enforcement points, run policy conformance tests, and inspect authorization logs for policy application., Inspect the asset inventory and mapping of owners/classifications to access controls.

Priority: Critical | Status: Pending

REQ-007: Experiment tracking with artifact versioning, metric collection, structured metadata, and visualizat…

Related Threats:

THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-014: Embeddings stored in vector DBs contain encoded PII or sensitive training data w…
THR-015: Attacker registers a malicious model artifact with forged metadata (faking prove…
THR-018: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide m…
…and 5 more threats

Security Controls:

[OWASP] ASVS 10.3: [OWASP] Protect data integrity and provide mechanisms for versioning and tamper-evidence…
[NIST] CM-3: [NIST] Configuration Change Control: The organization develops, documents, and maintain…
[ISO27001] A.12.5.1: [ISO27001] Changes to information processing facilities and systems should be controlled to…

Verification: Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications., Review change tracking records, test ability to view diffs between versions, and confirm baselines exist for experiments.

Priority: Critical | Status: Pending

REQ-008: Side-by-side experiment comparison UI with ability to select multiple runs, align metrics, and compa…

Related Threats:

THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-018: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide m…
THR-021: Insufficient logging/auditing of prompts, LLM responses, and system decisions le…
THR-024: Secrets or service credentials accidentally exposed in CI/CD build logs or pipel…
…and 4 more threats

Security Controls:

[OWASP] ASVS 10.3: [OWASP] Protect data integrity and provide mechanisms for versioning and tamper-evidence…
[NIST] CM-3: [NIST] Configuration Change Control: The organization develops, documents, and maintain…
[ISO27001] A.12.5.1: [ISO27001] Changes to information processing facilities and systems should be controlled to…

Priority: Critical | Status: Pending

REQ-010: Activity feed and audit logging for project events (create/update/delete), role changes, logins, dep…

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
…and 5 more threats

Security Controls:

[OWASP] ASVS 11.1: [OWASP] Application must generate logs for security-relevant events and ensure logs cann…
[NIST] AU-2: [NIST] Audit Events: The information system must determine and record auditable events …
[ISO27001] A.12.4.1: [ISO27001] Event logs recording user activities, exceptions, and information security event…

Verification: Check log retention configurations, review access controls to logs, and confirm logs include required event types., Inspect logs for required events, attempt tampering to validate protections, and verify retention/rotation policies., Review audit event definitions, inspect sample logs, and validate completeness against event list.

Priority: Critical | Status: Pending

REQ-011: Conversational AI assistant accessible via chat UI, integrated into project context and respecting R…

Related Threats:

THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
THR-008: Compromised CI/CD or deployment credentials are used to deploy malicious or back…
…and 5 more threats

Security Controls:

[OWASP] ASVS 10.1: [OWASP] Sensitive data processed by application features like chat should be classified …
[NIST] PL-8: [NIST] Information Security Architecture: Ensure that system design includes protection…
[ISO27001] A.18.1.4: [ISO27001] Personal data shall be protected in accordance with relevant legislation and org…

Verification: Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms., Review architecture diagrams, validate data flows, and confirm presence of privacy controls and logging/monitoring for LLM services.

Priority: Critical | Status: Pending

REQ-012: Natural language search across experiments, models, datasets and chat history with access control fi…

Related Threats:

THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-010: Identity provider outage (OIDC/SAML provider failure or network partition) preve…
…and 5 more threats

Security Controls:

[OWASP] ASVS 9.3: [OWASP] Search functionality must enforce access control checks on results and avoid inf…
[NIST] AC-19: [NIST] Access Control for Mobile Devices and Remote Access: Limitations and protections…
[ISO27001] A.9.2.5: [ISO27001] Access rights should be reviewed to ensure that search and retrieval functions d…

Verification: Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls., Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns., Check results of access reviews, and validate that changes to permissions update search index behavior.

Priority: Critical | Status: Pending

REQ-013: AI-powered code suggestions and explanations for ML workflows with guardrails to prevent unsafe or p…

Related Threats:

THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
THR-008: Compromised CI/CD or deployment credentials are used to deploy malicious or back…
THR-010: Identity provider outage (OIDC/SAML provider failure or network partition) preve…
…and 3 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-014: Automated insights and recommendations derived from experiment results, with provenance metadata and…

Related Threats:

THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-014: Embeddings stored in vector DBs contain encoded PII or sensitive training data w…
THR-015: Attacker registers a malicious model artifact with forged metadata (faking prove…
THR-018: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide m…
…and 3 more threats

Security Controls:

[OWASP] ASVS 10.4: [OWASP] AI-generated outputs that may expose sensitive information must be sanitized and…
[NIST] SI-11: [NIST] Error Handling and Information Leakage: Applications must detect and protect aga…
[ISO27001] A.9.4.4: [ISO27001] Controls should be applied to tools and utilities that can access sensitive info…

Verification: Review logs to ensure prompt/output recording and filtering, test the system by feeding sensitive prompts and verifying redaction., Conduct tests that confirm detection of secret patterns, review human-in-the-loop approvals, and audit output logs for leakage incidents., Review access controls for AI tooling, usage logs, and approval workflows for privileged AI features.

Priority: Critical | Status: Pending

REQ-015: Context-aware help that understands the current project, user role and dataset context while enforci…

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-002: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m…
THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
…and 5 more threats

Security Controls:

[OWASP] ASVS 5.2: [OWASP] Contextual features must honor authorization policies and ensure that help or gu…
[NIST] AC-6(1): [NIST] Least Privilege Enforcement: Systems that provide context-based capabilities mus…
[ISO27001] A.7.1.2: [ISO27001] Users must be made aware of their security responsibilities; context-aware help …

Verification: Perform privilege escalation tests using contextual help and log analysis to ensure scoping is honored., Review help content against policy and gather user feedback on compliance alignment., Test help outputs in various roles/projects and confirm no unauthorized data is revealed.

Priority: High | Status: Pending

REQ-016: AI-generated experiment summaries and model cards with ability to review, edit and approve before pu…

Related Threats:

THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
THR-008: Compromised CI/CD or deployment credentials are used to deploy malicious or back…
THR-012: Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (…
…and 5 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-017: Per-user chat history storage with export and deletion controls, retention policy and ability to opt…

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
…and 5 more threats

Security Controls:

[OWASP] ASVS 10.6: [OWASP] Provide mechanisms for users to export their data and request deletion; systems …
[NIST] MP-6: [NIST] Media Sanitization: Ensure data is properly removed from storage when deletion/e…
[ISO27001] A.18.1.4: [ISO27001] Implement procedures to support privacy rights, including data access, export, a…

Verification: Inspect sanitization procedures, attempt to recover deleted chat artifacts from backups, and verify logs of completed sanitization., Test export and deletion flows end-to-end including backups, and verify authenticity checks and propagation to dependent systems., Review data subject request procedures, test sample export-delete requests, and validate audit trail of actions.

Priority: Critical | Status: Pending

REQ-018: Multi-language support for the AI assistant including language detection and localization of system …

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
THR-013: Prompt injection attacks where user-supplied data or adversarial content manipul…
THR-017: Chat history export feature or saved transcripts accessible without proper autho…
THR-021: Insufficient logging/auditing of prompts, LLM responses, and system decisions le…
…and 1 more threats

Security Controls:

[OWASP] ASVS 10.1: [OWASP] Sensitive data processed by application features like chat should be classified …
[NIST] PL-8: [NIST] Information Security Architecture: Ensure that system design includes protection…
[ISO27001] A.18.1.4: [ISO27001] Personal data shall be protected in accordance with relevant legislation and org…

Priority: Critical | Status: Pending

REQ-019: Dataset upload and management via web UI and APIs with file validation, virus scanning, and optional…

Related Threats:

THR-001: Phishing or credential-theft attack (including stolen password or OAuth tokens) …
THR-002: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-013: Prompt injection attacks where user-supplied data or adversarial content manipul…
…and 5 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

REQ-020: Data preview for tabular, image and structured data in browser with row/column-level access controls…

Related Threats:

THR-003: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio…
THR-004: SQL injection or other injection vulnerabilities in APIs or query builders allow…
THR-005: WebSocket/real-time channels leaking sensitive project comments, chat content or…
THR-006: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per…
THR-007: Sensitive context (PII, secret keys, model IP) included in prompts or context wi…
…and 5 more threats

Verification: Manual Review

Priority: Medium | Status: Pending

Showing detailed mappings for 20 of 35 requirements.

Appendix E: References

End of Report - Generated by Security Requirements Analysis System v2.0 Generated: 2025-11-19 16:47:15

--- title: "Security Requirements Analysis Report" subtitle: "Comprehensive Security Analysis with Interactive Dashboard" author: "Security Requirements System v2.0" date: "2025-11-19 16:47:15" format: html: page-layout: full toc: true toc-depth: 3 toc-location: left embed-resources: true code-fold: true code-tools: true fig-width: 8 fig-height: 5 fig-dpi: 300 number-sections: false smooth-scroll: true mermaid-format: svg execute: echo: false warning: false message: false freeze: auto jupyter: python3 --- *Generated: 2025-11-19 16:47:15* *Report Version: 2.0 - Comprehensive Security Analysis* --- ## 1. Executive Summary This section provides a high-level overview of the security requirements analysis, presenting key findings, validation results, and an interactive dashboard for stakeholders and decision-makers. The executive summary enables rapid comprehension of the security posture, critical risks, control coverage, and compliance status without requiring detailed technical knowledge. ### 1.1. Purpose and Scope **Purpose** This document presents a comprehensive security requirements analysis for the proposed application, systematically mapping high-level business requirements to specific, actionable security controls aligned with multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. The analysis provides a complete security requirements specification that guides secure system design, implementation, and verification. **Scope** This analysis encompasses all functional requirements provided, delivering comprehensive coverage across multiple security domains: - **Requirements Analysis**: Systematic decomposition and security-relevant extraction from business requirements - **Stakeholder Analysis**: Identification of stakeholders, trust boundaries, and security responsibilities - **Threat Modeling**: Systematic identification and assessment of security threats using STRIDE methodology - **Security Control Mapping**: Mapping requirements to multi-standard security controls (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed implementation guidance - **Compliance Requirements**: Identification of regulatory and legal compliance obligations - **Architectural Security**: Security architecture recommendations and design patterns - **Implementation Planning**: Prioritized, phased implementation roadmap - **Verification Strategies**: Testing and validation approaches for security controls The analysis provides both strategic guidance for security planning and tactical details for implementation teams. ### 1.2. Key Findings This section summarizes the most critical results from the security requirements analysis, providing executives and stakeholders with immediate insight into the security posture and validation status. **Analysis Metrics** - **Validation Score**: 0.89/1.0 - **Validation Status**: ✅ Passed - **Analysis Iterations**: 1 - **Requirements Analyzed**: 25 **Application Summary** A browser-first, multi-tenant web application for teams to manage end-to-end machine learning workflows — including project & experiment tracking, dataset and model management, deployments, monitoring, and collaboration — augmented by an integrated conversational AI assistant that provides search, code suggestions, insights, summaries and context-aware help while integrating securely with external ML platforms and cloud storage. The validation score reflects the quality and completeness of the security requirements across five dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. A score of 0.8 or higher indicates that the requirements are ready for implementation, while scores below this threshold may require refinement before proceeding. ### 1.3. Security Overview Dashboard This interactive dashboard provides executive-level visualization of key security metrics and trends, enabling rapid assessment of the security posture through intuitive charts and data visualizations. The dashboard presents critical information across multiple dimensions: risk distribution, security control coverage, compliance status, implementation progress, and data quality metrics. For optimal viewing experience, render this document with Quarto to enable interactive chart functionality, allowing stakeholders to explore data dynamically and drill down into specific areas of interest. ::: {.panel-tabset} #### Risk ```{python} #| label: load-metrics #| echo: false #| warning: false import json import pandas as pd from pathlib import Path # Load dashboard data artifacts artifacts_path = Path("artifacts_20251119_164715") threats = pd.read_json(artifacts_path / "threats.json") asvs = pd.read_json(artifacts_path / "asvs_mapping.json") priorities = pd.read_json(artifacts_path / "priorities.json") compliance = pd.read_json(artifacts_path / "compliance.json") delivery = pd.read_json(artifacts_path / "delivery.json") coverage = pd.read_json(artifacts_path / "coverage.json") validation = json.loads((artifacts_path / "validation.json").read_text()) ``` ```{python} #| label: fig-risk-heatmap #| fig-cap: "Risk heat map showing threat distribution by likelihood and impact (1-5 scale)." #| echo: false #| warning: false try: import plotly.express as px import plotly.graph_objects as go # Create risk heatmap heat = threats.groupby(["likelihood", "impact"]).size().reset_index(name="count") # Create a complete 5x5 grid import numpy as np l_range = range(1, 6) i_range = range(1, 6) grid_data = [] for l in l_range: for i in i_range: count = heat[(heat["likelihood"] == l) & (heat["impact"] == i)]["count"].sum() grid_data.append({"likelihood": l, "impact": i, "count": int(count)}) heat_df = pd.DataFrame(grid_data) pivot = heat_df.pivot(index="impact", columns="likelihood", values="count").fillna(0) fig_risk = go.Figure(data=go.Heatmap( z=pivot.values, x=[f"L{x}" for x in pivot.columns], y=[f"I{y}" for y in pivot.index], text=pivot.values, texttemplate="%{text:.0f}", textfont={"size": 12}, colorscale="YlOrRd", hoverongaps=False )) fig_risk.update_layout( title="Threat Risk Matrix (Likelihood × Impact)", xaxis_title="Likelihood →", yaxis_title="Impact →", height=400 ) fig_risk.show() except Exception as e: print(f"⚠️ Could not generate risk heatmap: {e}") print("\nRisk Distribution (Static):") print(threats.groupby("risk_level").size()) ``` **Top 5 Highest Risks:** ```{python} #| echo: false #| output: asis #| warning: false top_risks = threats.nlargest(5, ["likelihood", "impact"]) for idx, risk_row in top_risks.iterrows(): risk_id = risk_row['id'] risk_level = risk_row['risk_level'] component = risk_row.get('component', 'Unknown Component') category = risk_row.get('category', 'N/A') likelihood = risk_row.get('likelihood', 'N/A') impact = risk_row.get('impact', 'N/A') description = risk_row['description'] print(f"**{risk_id}** ({risk_level}) - {component}") print(f"- **Category:** {category}") print(f"- **Likelihood:** {likelihood} | **Impact:** {impact}") print(f"- **Description:** {description}") print("") ``` #### Controls ```{python} #| label: fig-standard-distribution #| fig-cap: "Security control distribution by standard (OWASP, NIST, ISO 27001)." #| echo: false #| warning: false try: import plotly.express as px # Group by standard if "standard" in asvs.columns: dist = asvs.groupby("standard").size().reset_index(name="controls") dist = dist.sort_values("controls", ascending=False) # Add percentage for better context total = dist["controls"].sum() dist["percentage"] = (dist["controls"] / total * 100).round(1) # Map standard codes to display names standard_names = { "OWASP": "OWASP ASVS", "NIST": "NIST SP 800-53", "ISO27001": "ISO 27001:2022" } dist["standard_name"] = dist["standard"].map(standard_names).fillna(dist["standard"]) fig_std = px.bar( dist, x="standard_name", y="controls", text=[f"{c}<br>({p}%)" for c, p in zip(dist["controls"], dist["percentage"])], title="Security Controls by Standard", labels={"standard_name": "Security Standard", "controls": "Control Count"}, color="controls", color_continuous_scale="Blues" ) fig_std.update_traces(textposition='outside') fig_std.update_layout(height=400, showlegend=False) fig_std.show() else: print("Standard distribution data not available.") except Exception as e: print(f"⚠️ Could not generate standard distribution chart: {e}") ``` ```{python} #| label: fig-asvs-distribution #| fig-cap: "OWASP ASVS control distribution by verification category (V1-V14)." #| echo: false #| warning: false try: import plotly.express as px # Filter to OWASP controls only asvs_owasp = asvs[asvs["standard"] == "OWASP"] if "standard" in asvs.columns else asvs # Group by ASVS category dist = asvs_owasp.groupby("v_category").size().reset_index(name="controls") dist = dist.sort_values("v_category") # Add percentage for better context total = dist["controls"].sum() dist["percentage"] = (dist["controls"] / total * 100).round(1) if total > 0 else 0 fig_asvs = px.bar( dist, x="v_category", y="controls", text=[f"{c}<br>({p}%)" for c, p in zip(dist["controls"], dist["percentage"])], title="OWASP ASVS Controls by Verification Category", labels={"v_category": "ASVS Category", "controls": "Control Count"}, color="controls", color_continuous_scale="Blues" ) fig_asvs.update_traces(textposition='outside') fig_asvs.update_layout(height=400, showlegend=False) fig_asvs.show() except Exception as e: print(f"⚠️ Could not generate ASVS chart: {e}") ``` ```{python} #| label: fig-priority-breakdown #| fig-cap: "Security control priority distribution (Critical/High/Medium/Low)." #| echo: false #| warning: false try: import plotly.express as px # Priority breakdown with color mapping color_map = { "Critical": "#c62828", "High": "#f57c00", "Medium": "#fbc02d", "Low": "#388e3c" } # Sort by priority level priority_order = ["Critical", "High", "Medium", "Low"] priorities_sorted = priorities.set_index("level").reindex(priority_order).reset_index() fig_prio = px.bar( priorities_sorted, x="level", y="count", text="count", title="Control Priority Breakdown", labels={"level": "Priority Level", "count": "Number of Controls"}, color="level", color_discrete_map=color_map ) fig_prio.update_traces(textposition='outside') fig_prio.update_layout(height=400, showlegend=False) fig_prio.show() except Exception as e: print(f"⚠️ Could not generate priority chart: {e}") ``` **Coverage Metrics:** ```{python} #| echo: false #| output: asis #| warning: false total_controls = len(asvs) total_reqs = len(coverage) req_coverage = (coverage["has_controls"].mean() * 100) if not coverage.empty else 0 verif_coverage = (coverage["tests"].gt(0).mean() * 100) if not coverage.empty else 0 # Calculate additional metrics avg_controls_per_req = (total_controls / total_reqs) if total_reqs > 0 else 0 critical_controls = len(asvs[asvs["priority"] == "Critical"]) if "priority" in asvs.columns else 0 # Calculate standard distribution if available if "standard" in asvs.columns: std_counts = asvs["standard"].value_counts() owasp_count = std_counts.get("OWASP", 0) nist_count = std_counts.get("NIST", 0) iso_count = std_counts.get("ISO27001", 0) print(f"- **Total Security Controls Mapped:** {total_controls}") print(f" - OWASP ASVS: {owasp_count} controls") print(f" - NIST SP 800-53: {nist_count} controls") print(f" - ISO 27001: {iso_count} controls") else: print(f"- **Total Security Controls Mapped:** {total_controls} (OWASP ASVS, NIST SP 800-53, ISO 27001)") print(f"- **Requirements with Security Control Mapping:** {req_coverage:.1f}% ({coverage['has_controls'].sum()}/{total_reqs})") print(f"- **Average Controls per Requirement:** {avg_controls_per_req:.1f}") print(f"- **Critical Controls:** {critical_controls} ({critical_controls/total_controls*100:.1f}% of total)" if total_controls > 0 else "- **Critical Controls:** {critical_controls}") print(f"- **Requirements with Verification:** {verif_coverage:.1f}% ({coverage['tests'].gt(0).sum()}/{total_reqs})") print(f"- **Recommended ASVS Level:** L2 (Standard)") ``` #### Compliance ```{python} #| label: fig-compliance-rag #| fig-cap: "Compliance status across all applicable frameworks (Red-Amber-Green rating). Shows regulatory compliance (GDPR, HIPAA, PCI-DSS, etc.) and security standards (OWASP ASVS, NIST SP 800-53, ISO 27001)." #| echo: false #| warning: false try: import plotly.express as px # Show all compliance frameworks (regulatory and security standards) comp_filtered = compliance # Compliance RAG status status_map = { "Compliant": "#2e7d32", "In Progress": "#f9a825", "Gap": "#c62828" } # Sort frameworks alphabetically comp_sorted = comp_filtered.sort_values("framework") fig_comp = px.bar( comp_sorted, x="framework", y=[1] * len(comp_sorted), # Equal height bars color="status", color_discrete_map=status_map, title="Compliance Status Across All Frameworks", labels={"framework": "Framework", "y": ""}, text="status", height=400 ) fig_comp.update_layout(showlegend=True, yaxis_visible=False, yaxis_showticklabels=False) fig_comp.update_traces(textposition='inside') fig_comp.show() except Exception as e: print(f"⚠️ Could not generate compliance chart: {e}") print("\nCompliance Status (Static):") for _, row in compliance.iterrows(): print(f"- **{row['framework']}**: {row['status']}") ``` **Compliance Summary:** ```{python} #| echo: false #| output: asis #| warning: false for _, row in compliance.iterrows(): status_icon = "✅" if row["status"] == "Compliant" else "⚠️" if row["status"] == "In Progress" else "❌" print(f"- {status_icon} **{row['framework']}**: {row['status']} (Next Audit: {row['next_audit']})") ``` #### Delivery ```{python} #| label: fig-delivery-burndown #| fig-cap: "Projected implementation timeline by phase and week (based on priority-based planning)." #| echo: false #| warning: false try: import plotly.express as px import plotly.graph_objects as go # Create burndown chart with planned vs projected completion fig_burn = go.Figure() for phase in delivery["phase"].unique(): phase_data = delivery[delivery["phase"] == phase] # Planned line fig_burn.add_trace(go.Scatter( x=phase_data["week"], y=phase_data["planned"], mode='lines', name=f"{phase} (Planned)", line=dict(dash='dash', color='gray'), showlegend=True )) # Projected completion line fig_burn.add_trace(go.Scatter( x=phase_data["week"], y=phase_data["completed"], mode='lines+markers', name=f"{phase} (Projected)", showlegend=True )) fig_burn.update_layout( title="Security Controls Implementation Timeline (Projected)", xaxis_title="Week", yaxis_title="Number of Controls", height=400, hovermode='x unified' ) fig_burn.show() except Exception as e: print(f"⚠️ Could not generate delivery chart: {e}") ``` **Implementation Timeline (Projected):** ```{python} #| echo: false #| output: asis #| warning: false phase1 = delivery[delivery["phase"].str.contains("Phase 1")] phase2 = delivery[delivery["phase"].str.contains("Phase 2")] if not phase1.empty: p1_progress = (phase1["completed"].iloc[-1] / phase1["planned"].iloc[-1] * 100) if phase1["planned"].iloc[-1] > 0 else 0 print(f"- **Phase 1 (Critical/High):** {p1_progress:.0f}% projected completion (Weeks 1-8)") if not phase2.empty: p2_progress = (phase2["completed"].iloc[-1] / phase2["planned"].iloc[-1] * 100) if phase2["planned"].iloc[-1] > 0 else 0 print(f"- **Phase 2 (Medium):** {p2_progress:.0f}% projected completion (Weeks 9-16)") print(f"- **Phase 3 (Low/Ongoing):** Continuous improvement and monitoring") print(f"") print(f"*Note: Timeline is based on priority-based planning and assumes steady implementation progress.*") ``` #### Data Quality **Validation Metrics:** ```{python} #| echo: false #| output: asis #| warning: false val_score = validation.get("score", 0) val_passed = validation.get("passed", False) dims = validation.get("dims", {}) status_icon = "✅" if val_passed else "⚠️" if val_score >= 0.7 else "❌" print(f"") print(f"**Overall Validation Score:** {status_icon} {val_score:.2f}/1.0") print(f"") if dims: print("**Dimension Scores:**") print(f"") for dim, score in dims.items(): dim_icon = "✅" if score >= 0.8 else "⚠️" if score >= 0.7 else "❌" print(f"- {dim_icon} **{dim.capitalize()}:** {score:.2f}") else: print("*Dimension scores not available.*") ``` ```{python} #| label: fig-data-quality #| fig-cap: "Data quality and coverage metrics." #| echo: false #| warning: false try: import plotly.graph_objects as go # Calculate quality metrics threats_linked_pct = (coverage["has_threat"].mean() * 100) if not coverage.empty else 0 # Calculate average controls per requirement avg_controls_per_req = (total_controls / total_reqs) if total_reqs > 0 else 0 # For the chart, we'll show percentage-based metrics and handle avg controls separately metrics = { "Requirements Mapped": req_coverage, "Threats Linked": threats_linked_pct, "Verification Coverage": verif_coverage, "Avg Controls/Req": avg_controls_per_req * 20, # Scale for visual comparison (multiply by 20 to fit chart scale) "Validation Score": val_score * 100 } # Create custom text labels (show actual values for avg controls, percentages for others) text_labels = [] for key, val in metrics.items(): if key == "Avg Controls/Req": text_labels.append(f"{avg_controls_per_req:.1f}") else: text_labels.append(f"{val:.1f}%") fig_quality = go.Figure(go.Bar( x=list(metrics.values()), y=list(metrics.keys()), orientation='h', text=text_labels, textposition='outside', marker=dict( color=list(metrics.values()), colorscale='RdYlGn', cmin=0, cmax=100 ) )) fig_quality.update_layout( title="Data Quality & Coverage Metrics", xaxis_title="Score / Percentage (%)", xaxis=dict(range=[0, 110]), height=400, showlegend=False ) fig_quality.show() except Exception as e: print(f"⚠️ Could not generate quality chart: {e}") ``` ```{python} #| echo: false #| output: asis #| warning: false # Parser and data quality stats total_entries = len(coverage) with_threats = coverage["has_threat"].sum() with_controls = coverage["has_controls"].sum() with_tests = coverage["tests"].gt(0).sum() print(f"") print(f"**Traceability Matrix:**") print(f"") print(f"- Total Requirements: {total_entries}") print(f"- Linked to Threats: {with_threats} ({with_threats/max(total_entries,1)*100:.1f}%)") print(f"- Mapped to Security Controls: {with_controls} ({with_controls/max(total_entries,1)*100:.1f}%)") print(f"- With Verification: {with_tests} ({with_tests/max(total_entries,1)*100:.1f}%)") print(f"") print(f"**Data Quality:** {"✅ Excellent" if val_score >= 0.8 else "⚠️ Good" if val_score >= 0.7 else "❌ Needs Improvement"}") ``` ::: --- ## 2. Requirements Understanding This section presents a comprehensive analysis of the functional requirements, extracting security-relevant information and establishing the foundation for the security requirements specification. Understanding the functional requirements is essential for identifying security implications, data sensitivity, trust boundaries, and security-critical components. This analysis transforms business requirements into security-aware specifications that inform threat modeling, control selection, and compliance assessment. ### 2.1. High-Level Requirements Analysis The following high-level functional requirements have been identified and analyzed for security implications: 1. User registration and login with email/password and social OAuth (Google, GitHub) 2. Single Sign-On (SSO) via OIDC and SAML for enterprise customers 3. Role-based access control (Admin, Project Manager, Data Scientist, Viewer) and team workspaces 4. User profile management with avatar uploads, preferences, and 2FA support 5. Create and organize ML projects with metadata, tags, and access controls 6. Experiment tracking with versioning, metrics, artifacts, visualizations and comparison UI 7. Save and share experiment configurations as templates and project-level sharing settings 8. Activity feed and audit logs for project and system changes 9. Conversational AI assistant (LLM) with chat UI, multi-language support and saved chat history 10. Natural-language search across experiments, models, datasets and chat history 11. AI-powered code suggestions, explanations, experiment summaries, and automated insights 12. Context-aware help respecting current project context and user role 13. Chat export and per-user chat history controls (export/delete) 14. Dataset upload, preview (tables/images/structured data), and data versioning/lineage 15. Interactive metric visualization and drag-and-drop custom dashboards with export (image/PDF) 16. Model registry with metadata, versioning, comparison, and model card generation 17. Deploy models to staging and production via UI and support A/B testing and rolling deployments 18. Model monitoring dashboard (accuracy, drift, latency) and alerting 19. Commenting, threaded discussions, @mentions and real-time collaboration on projects 20. Public project showcase with opt-in sharing controls 21. Integration adapters for cloud storage (S3/GCS/Azure), external ML platforms and CI/CD 22. Secure APIs for artifact storage, model deployment, and telemetry ingestion 23. Data retention, export, deletion and data subject rights workflows for compliance 24. Audit, logging, encryption, and monitoring for security and operational observability 25. Administrative features: tenant settings, billing integration, usage reporting, and quota management ### 2.2. Detailed Requirements Breakdown | Req ID | Requirement | Business Category | Security Sensitivity | Data Classification | |--------|-------------|-------------------|---------------------|---------------------| | REQ-001 | User registration and login supporting email/passw... | Authentication | High | Restricted | | REQ-002 | Enterprise Single Sign-On (SSO) via OIDC and SAML ... | Authentication | High | Restricted | | REQ-003 | Role-based access control (RBAC) supporting Admin,... | Authorization | High | Confidential | | REQ-004 | Team workspaces allowing shared projects, membersh... | Collaboration | Medium | Confidential | | REQ-005 | User profile management including avatar uploads, ... | User Experience / Authentication | Medium | Internal | | REQ-006 | Project creation and organization with description... | Project Management | Medium | Confidential | | REQ-007 | Experiment tracking with artifact versioning, metr... | Experiment Management | High | Confidential | | REQ-008 | Side-by-side experiment comparison UI with ability... | Experiment Management / UX | Medium | Confidential | | REQ-009 | Save and share experiment configurations as reusab... | Experiment Management | Medium | Confidential | | REQ-010 | Activity feed and audit logging for project events... | Audit & Compliance | High | Restricted | | REQ-011 | Conversational AI assistant accessible via chat UI... | AI Assistant | High | Confidential | | REQ-012 | Natural language search across experiments, models... | Search / Data Access | High | Confidential | | REQ-013 | AI-powered code suggestions and explanations for M... | AI Assistant / Developer Productivity | High | Confidential | | REQ-014 | Automated insights and recommendations derived fro... | AI Assistant / Analytics | Medium | Confidential | | REQ-015 | Context-aware help that understands the current pr... | AI Assistant / UX | Medium | Confidential | | REQ-016 | AI-generated experiment summaries and model cards ... | Reporting / Model Governance | Medium | Confidential | | REQ-017 | Per-user chat history storage with export and dele... | AI Assistant / Data Management | High | Confidential | | REQ-018 | Multi-language support for the AI assistant includ... | UX / Internationalization | Low | Public | | REQ-019 | Dataset upload and management via web UI and APIs ... | Data Management | High | Confidential | | REQ-020 | Data preview for tabular, image and structured dat... | Data Management / UX | Medium | Confidential | | REQ-021 | Interactive charts and visualizations for experime... | Visualization | Medium | Confidential | | REQ-022 | Data versioning and lineage tracking for datasets ... | Data Management / Governance | High | Confidential | | REQ-023 | Model registry with metadata, artifact storage, ve... | Model Management | High | Confidential | | REQ-024 | Model deployment from UI to staging and production... | Deployment / Integrations | High | Confidential | | REQ-025 | Model monitoring dashboard including prediction ac... | Monitoring / Ops | High | Confidential | | REQ-026 | A/B testing and traffic-splitting capabilities for... | Deployment / Experimentation | Medium | Confidential | | REQ-027 | Comments, threaded discussions, @mentions, notific... | Collaboration | Medium | Internal | | REQ-028 | Public project showcase feature that allows opt-in... | Sharing / Publication | Medium | Public | | REQ-029 | Integration adapters for cloud storage (S3, GCS, A... | Integrations | High | Confidential | | REQ-030 | Secure REST/GraphQL APIs for programmatic access t... | APIs / Platform | High | Confidential | | REQ-031 | Encryption in transit (TLS) for all communications... | Security / Data Protection | High | Restricted | | REQ-032 | Data retention, export, and deletion workflows to ... | Compliance / Data Management | High | Confidential | | REQ-033 | Administrative controls for tenant settings, billi... | Administration / Governance | High | Internal | | REQ-034 | Operational monitoring, alerting and incident resp... | Security Operations | High | Internal | | REQ-035 | Privacy and safety controls for the AI assistant a... | AI Safety / Privacy | High | Confidential | ### 2.3. Security Context and Regulatory Obligations Applicable regulations and compliance obligations include: GDPR (data subject rights, data minimization, DPIAs, data transfers) for EU users; CCPA/CPRA for California consumer data rights; SOC 2 and ISO 27001 as enterprise security/custodian standards for controls and audits; HIPAA when customers process protected health information (require BAAs, PHI controls, audit trails and encryption); PCI-DSS only if platform handles payment cardholder data (minimize/avoid storage); FedRAMP and government-specific requirements for US government tenants; export control and trade compliance for model/technology; NIST Cybersecurity Framework and emerging AI-specific frameworks (NIST AI RMF, EU AI Act considerations) for model governance, explainability and risk management. Additional obligations: data residency/localization enforced per-tenant, breach notification timelines, consent capture for personal data and user opt-outs for use of content in model training. ### 2.4. Assumptions - Platform will be cloud-hosted (public cloud with multi-region support) with support for customer-managed keys (BYOK) where required - Users have internet access and modern browsers (browser-first UI) - External LLMs and third-party services (storage, ML infra) may be used and are accessible via secure APIs - Enterprise customers will provide identity providers for SSO (OIDC/SAML) and may require SCIM for provisioning - Customers may upload sensitive or regulated data (PII/PHI/proprietary models) - Model deployment targets (staging/prod) will be reachable via secure network connectivity (VPC peering, private endpoints) - System will operate as a multi-tenant SaaS with logical isolation per tenant and optional dedicated tenancy for some enterprise customers ### 2.5. Constraints - Must integrate with existing external ML platforms and cloud storage (S3/GCS/Azure) via available APIs and not require deep changes to customer infra - Needs to support large datasets and artifacts; storage and bandwidth costs are a constraint and may require tiered storage/archival policies - Latency and cost constraints for AI assistant when using remote LLM APIs — may require caching, on-prem or private LLM deployment options for enterprises - Regulatory constraints for data residency may require per-region deployments or customer-managed infrastructure - Support for enterprise SSO, SCIM and custom attribute mappings increases integration complexity and testing effort - Real-time collaboration and presence features create scalability and state-management constraints (WebSocket/RTC infrastructure) - Model deployment must support heterogeneous runtime targets (Kubernetes, managed inference services) and adhere to customer security policies, which constrains feature parity across targets - Retention and deletion requirements for audit/log data may conflict with forensic needs and require configurable retention profiles - Limited offline or air-gapped deployment capabilities unless offering a dedicated on-premises or isolated/cloud-stack option --- ## 3. Stakeholder Analysis This section identifies and analyzes all stakeholders involved in or affected by the system, including users, administrators, external partners, and regulatory bodies. Stakeholder analysis establishes trust boundaries, defines security responsibilities, and identifies potential security concerns from different stakeholder perspectives. Understanding stakeholder relationships and trust boundaries is critical for designing appropriate access controls, authentication mechanisms, and data protection measures. ### 3.1. Identified Stakeholders and User Personas | Role | Privilege Level | Trust Level | Key Security Concerns | |-------------------------|-----------------|------------------|----------------------------------------------------------------------------------------| | Admin | Admin | Trusted | Privilege escalation risks, unauthorized access to sensitive data, and insider threats. | | Project Manager | User | Partially Trusted | Mismanagement of project permissions, data leakage during collaboration, and phishing. | | Data Scientist | User | Partially Trusted | Exposure of proprietary models, unauthorized data access, and misuse of AI capabilities. | | Viewer | User | Untrusted | Limited access; concerns mainly around unauthorized information sharing or viewing. | | AI Assistant | Service Account | Trusted | Vulnerability to adversarial input, data leakage, and reliance on external LLM security. | | External ML Platform | Service Account | Partially Trusted | Insecure API interactions, data exposure during integration, and lack of proper authentication. | | Cloud Storage Provider | Service Account | Partially Trusted | Data breaches, insufficient encryption practices, and unauthorized access to stored data. | | CI/CD Pipeline | Service Account | Trusted | Risks of deploying unverified code, privilege escalation during deployment processes, and exposure to vulnerabilities. | ### 3.2. Trust Model Trust boundaries are established at the user interface, backend server, and database levels. Security mechanisms enforcing these boundaries include user authentication methods such as email/password, social OAuth, and SSO for enterprise customers, along with role-based access control (RBAC) to ensure users can only access data and functionalities pertinent to their roles. Network segmentation is also utilized to mitigate risks of unauthorized access. Admins have comprehensive management access, allowing them to oversee all projects and user activities. Project Managers can access specific project data and manage team collaboration without compromising sensitive information. Data Scientists are granted access to experiment data and model management, ensuring they can conduct analyses without exposing sensitive data unnecessarily. Viewers have limited access, restricted to viewing shared experiments and projects only. The AI Assistant is designed to access necessary data for user assistance without compromising security, while third-party integrations are tightly controlled through secure APIs. The principle of least privilege is implemented by granting users only the minimum access necessary to perform their responsibilities, thereby reducing the risk of data exposure and privilege escalation. --- ## 4. System Architecture Analysis ### 4.1. Architectural Overview A cloud-hosted, browser-first multi-tenant web platform composed of a secured frontend SPA served via an edge CDN/WAF, a central API gateway and application service layer that implements project/experiment/model management, an AI assistant orchestration layer that mediates LLM usage, real-time collaboration services, and integration adapters to external ML platforms and cloud storage. Persistent storage is split into relational metadata, object artifact storage, vector/search store for embeddings, time-series metrics, and append-only audit logs. CI/CD and a deployment orchestrator target runtime clusters for model serving. Monitoring, SIEM and key management integrate for encryption, observability and compliance. All external interactions (identity providers, LLMs, cloud storage, ML infra) occur over secure, authenticated APIs with RBAC, tenant isolation and audit trails. ### 4.2. Architecture Diagram <style> svg { width: 100% !important; max-width: 100% !important; } .nodeLabel { white-space: normal !important; } </style> ```{mermaid} %%{init: {'theme':'dark'}}%% graph TB subgraph "Users" U1[Users Browsers] --> EDGE[Edge CDN & WAF] end subgraph "Frontend Layer" EDGE --> FE[Web App SPA & Admin UI] FE --> API[API Gateway & Auth Proxy] end subgraph "Application Services" API --> AS[Core App Services Projects/Experiments/Models] AS --> AI[AI Assistant & LLM Orchestration] AS --> RT[Realtime Collaboration & Notifications] AS --> INTEG[Integration Adapters & Webhooks] end subgraph "Data Layer" AS --> DB[Relational DB Tenancy RBAC Metadata] AS --> OBJ[Object Storage Artifacts/Datasets] AS --> TS[Time-series DB Metrics] AS --> VEC[Vector DB Search/Embeddings] AS --> AUDIT[Audit Logs AppendOnly Storage] end subgraph "Platform & Ops" AS --> CI[CI/CD & Deployment Orchestrator] CI --> K8S[Runtime & Model Serving Platform] AS --> MON[Monitoring & SIEM] AS --> KMS[Key Management Service / BYOK] end subgraph "External Services" AI --> LLM[External LLM Providers / Private LLMs] OBJ --> CloudStorage[S3 GCS Azure Blob] API --> IDP[Identity Providers OIDC SAML Social] INTEG --> ExtML[External ML Platforms SageMaker/Kubeflow] CI --> GitCI[CI Systems & Container Registries] end %% cross links FE --> RT RT --> DB AUDIT --> MON K8S --> MON K8S --> OBJ ``` ### 4.3. Component Breakdown | Component | Responsibility | Security Criticality | External Dependencies | |-----------|----------------|---------------------|----------------------| | Frontend Layer | Provides the browser-first SPA and admin... | High | CDN/WAF providers, Identity Providers (OIDC/SAML/Social OAuth) | | Edge / API Gateway | Ingress control for traffic (TLS termina... | Critical | Cloud Load Balancers, WAF/CDN services | | Application Services | Core business logic for projects, experi... | Critical | Relational DB, Object Storage | | AI Assistant & LLM Orchestration | Middleware that sanitizes context, enfor... | Critical | External LLM providers, Vector DB for embeddings | | Realtime Collaboration & Notifications | Handles WebSocket/RTC or similar channel... | High | Managed realtime services or self-hosted socket layer | | Data Storage | Stores persistent data: relational DB fo... | Critical | Cloud object stores (S3/GCS/Azure Blob), Managed DB services | | Integration & Deployment Services | Adapters to external ML platforms, cloud... | High | SageMaker/Kubeflow/APIs, CI systems | | Platform & Ops | Operational tooling: CI/CD, runtime clus... | Critical | SIEM/Monitoring vendors, KMS providers | | External Services | Third-party dependencies used for identi... | High | Identity Providers (OIDC/SAML), LLM APIs (OpenAI/Anthropic/private) | ### 4.4. Data Flow Analysis Users interact via the SPA which calls the API Gateway after authentication. Requests go to Core App Services which enforce RBAC, fetch metadata from the relational DB and metrics from the time-series DB, and retrieve artifacts from object storage. The AI Assistant orchestrator requests embeddings from the vector DB and routes sanitized prompts to LLM providers, storing chat history and summaries in per-user storage with retention controls. Realtime collaboration messages traverse the realtime service and may be persisted as comments. Deployments are triggered through CI/CD to the runtime platform which pulls model artifacts from object storage. Audit logs and telemetry stream to append-only storage and monitoring/SIEM for detection and compliance. Integrations to cloud storage and external ML platforms use scoped service accounts and token-based APIs; sensitive keys are managed via KMS/BYOK. ### 4.5. Attack Surface Analysis Primary entry points: Web UI (SPA) and public API Gateway (REST/GraphQL) — high risk if auth or input validation is weak; mitigate with strong auth, rate limiting, WAF, and strict content scanning. Authentication flows and SSO endpoints (OIDC/SAML/social) are critical compromise targets — use hardened libraries, signature validation, JWS/JWT checks and monitoring. File upload pipeline (dataset/artifact uploads) presents malware and exfiltration risk — require virus scanning, content-type validation, PII/PHI detection, and staging quarantine. AI Assistant and LLM integrations expose potential data leakage and prompt injection risks — use context redaction, allowlist/blocklist, response filtering, prompt integrity checks, per-tenant opt-outs and logging. Integration adapters (cloud storage, external ML infra, CI/CD) and webhooks are lateral movement vectors — enforce least privilege, scoped tokens, IP allowlists and robust auditing. Realtime collaboration endpoints (WebSocket/RTC) require authentication and message size/validation controls to avoid abuse and data leakage. Exposed telemetry/monitoring endpoints must be authenticated and rate-limited to prevent information disclosure. Overall risk levels: Authentication & data storage (Critical), API surface & integrations (High), AI assistant & LLM paths (High), File upload & preview (High), Realtime/notifications (Medium), Public project showcase/export features (Medium) — mitigations include strong encryption, RBAC, tenant isolation, KMS/BYOK, privacy controls, input/output sanitization, SIEM integration and incident response runbooks. --- ## 5. Threat Modeling This section presents a comprehensive threat analysis of the system architecture and functional requirements. Threat modeling systematically identifies potential security vulnerabilities and attack vectors, enabling proactive risk mitigation through the application of appropriate security controls. ### 5.1. Threat Modeling Methodology This analysis employs the **STRIDE** threat modeling methodology, a systematic framework developed by Microsoft for identifying security threats across six categories: - **Spoofing Identity**: Threats involving impersonation of users or systems - **Tampering with Data**: Threats involving unauthorized modification of data or system components - **Repudiation**: Threats where users deny performing actions (lack of non-repudiation) - **Information Disclosure**: Threats involving unauthorized access to sensitive information - **Denial of Service**: Threats causing disruption or unavailability of system services - **Elevation of Privilege**: Threats allowing unauthorized access to privileged functions For each identified threat, the analysis evaluates **likelihood** (attack complexity and exposure) and **impact** (potential damage to confidentiality, integrity, or availability) to determine overall **risk level**. The methodology ensures comprehensive coverage of security concerns across all system components and interfaces. ### 5.2. Threat Analysis and Risk Assessment #### 5.2.1. Threat Overview The following table provides a quick reference of all identified threats. Detailed analysis including descriptions, mitigation strategies, and residual risk assessment (where available) is provided in the section below. | Threat ID | Component | Category | Risk Level | Likelihood | Impact | |-----------|-----------|----------|------------|-----------|--------| | THR-001 | Frontend Layer / User Management & Authentication | Spoofing | Critical | High | High | | THR-003 | Frontend Layer | Tampering | Critical | High | High | | THR-011 | Edge / API Gateway | Denial of Service | Critical | High | High | | THR-013 | AI Assistant & LLM Orchestration | Tampering | Critical | High | High | | THR-002 | Edge / API Gateway | Spoofing | High | Medium | High | | THR-004 | Application Services (Relational DB access) | Tampering | High | Medium | High | | THR-005 | Realtime Collaboration & Notifications | Information Disclosure | High | Medium | High | | THR-006 | Data Storage (Object Storage) | Information Disclosure | High | Medium | High | | THR-007 | AI Assistant & LLM Orchestration | Information Disclosure | High | Medium | High | | THR-008 | Integration & Deployment Services (CI/CD) | Elevation of Privilege | High | Medium | High | | THR-009 | Platform & Ops (Audit logs / Backups) | Tampering | High | Medium | High | | THR-014 | Data Storage (Vector DB for embeddings) | Information Disclosure | High | Medium | High | | THR-015 | Model Registry & Deployment | Spoofing | High | Medium | High | | THR-016 | Application Services (RBAC & Authorization) | Elevation of Privilege | High | Medium | High | | THR-019 | Edge / API Gateway / Frontend | Information Disclosure | High | Medium | High | | THR-021 | External LLM Providers / AI Assistant | Repudiation | High | High | Medium | | THR-022 | Frontend / Social OAuth flows | Spoofing | High | Medium | High | | THR-023 | Data Management & Visualization (Dataset uploads) | Tampering | High | Medium | High | | THR-024 | Integration & Deployment Services (CI/CD logs) | Information Disclosure | High | Medium | High | | THR-025 | Platform & Ops (KMS/Key management) | Elevation of Privilege | High | Low | High | | THR-026 | Application Services / Data Storage (Multi-tenant DB) | Information Disclosure | High | Medium | High | | THR-027 | Data Storage (Append-only Audit Logs) | Repudiation | High | Medium | High | | THR-028 | Frontend Layer / CDN | Tampering | High | Medium | High | | THR-029 | AI Assistant & LLM Orchestration | Denial of Service | High | High | Medium | | THR-030 | External Services (External ML Platforms & Cloud Storage Connectors) | Information Disclosure | High | Medium | High | | THR-010 | External Services (Identity Providers) | Denial of Service | Medium | Medium | Medium | | THR-012 | Frontend Layer / Application Services | Tampering | Medium | Medium | Medium | | THR-017 | AI Assistant & Frontend (Chat history) | Information Disclosure | Medium | Medium | Medium | | THR-018 | Data Storage (Time-series DB for metrics) | Tampering | Medium | Medium | Medium | | THR-020 | Realtime Collaboration & Notifications | Denial of Service | Medium | Medium | Medium | **Total Threats Identified:** 30 #### 5.2.2. Detailed Threat Analysis This section provides comprehensive analysis of each identified threat, including descriptions, mitigation strategies, and residual risk assessment (where controls have been evaluated). Threats are organized by risk level for prioritized review. ##### Critical Risk Threats **THR-001** - Frontend Layer / User Management & Authentication - **Category:** Spoofing - **Likelihood:** High | **Impact:** High - **Initial Risk Level:** Critical - **Description:** Phishing or credential-theft attack (including stolen password or OAuth tokens) leading to account takeover. Social OAuth flows or email/password login can be abused; social engineering of SSO sessions or interception of session tokens (e.g., via XSS or insecure storage) enables impersonation. - **Mitigation Strategy:** Enforce strong password policies, offer mandatory 2FA (prefer hardware or OTP), secure session cookies (HttpOnly, Secure, SameSite=strict), PKCE for OAuth flows, validate OAuth state and redirect_uris, implement phishing-resistant auth options (FIDO2), limit session duration, device approval workflows, session anomaly detection, educate users. - **Controls Applied:** 2FA (FIDO2/TOTP), Secure cookie flags, PKCE and OAuth state validation, Session anomaly detection - **Control Effectiveness:** Medium - **Residual Risk Level:** High - **Status:** ⚠️ Requires Review **THR-003** - Frontend Layer - **Category:** Tampering - **Likelihood:** High | **Impact:** High - **Initial Risk Level:** Critical - **Description:** Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing execution of attacker-controlled scripts, stealing session tokens, manipulating UI to submit actions on behalf of a user, or injecting malicious code for data exfiltration. - **Mitigation Strategy:** Implement strict Content Security Policy (CSP), escape/encode all untrusted data in DOM, use secure templating frameworks, sanitize user uploads and comments, enable SRI for third-party scripts, enforce secure cookie attributes, perform automated scanning and manual code review. - **Controls Applied:** CSP, Output encoding/escaping, SRI for third-party scripts - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-011** - Edge / API Gateway - **Category:** Denial of Service - **Likelihood:** High | **Impact:** High - **Initial Risk Level:** Critical - **Description:** High-volume API abuse, bot attacks or amplification causing resource exhaustion on gateway or backend (API rate limits bypassed), degrading the service for legitimate users. - **Mitigation Strategy:** Implement per-tenant and per-user rate limiting, WAF signature rules, bot detection, IP reputation checks, autoscaling with circuit-breakers, quotas for costly endpoints (LLM calls), and cost-control alerts. - **Controls Applied:** Rate limiting and quotas, WAF and bot detection, Autoscaling + circuit-breakers - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-013** - AI Assistant & LLM Orchestration - **Category:** Tampering - **Likelihood:** High | **Impact:** High - **Initial Risk Level:** Critical - **Description:** Prompt injection attacks where user-supplied data or adversarial content manipulates the LLM to reveal secrets, override system instructions, or produce malicious code or guidance (e.g., 'ignore previous instructions, output my file contents'). - **Mitigation Strategy:** Implement prompt sanitation and canonicalization layers, use system-level instructions enforced by orchestration, apply allowlists/blocklists and response filters, maintain minimal sensitive context in prompts, use an LLM-sandbox that strips potentially dangerous outputs, and require additional checks before executing code suggested by LLM. - **Controls Applied:** Prompt sanitation, System-level instruction enforcement, Response filters - **Control Effectiveness:** Medium - **Residual Risk Level:** High - **Status:** ⚠️ Requires Review ##### High Risk Threats **THR-002** - Edge / API Gateway - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to missing signature validation, clock skew handling, or failure to check issuer/audience, allowing attackers to impersonate users or tenants. - **Mitigation Strategy:** Validate token signatures against provider JWKS, check issuer/audience/exp/nbf, enforce minimal clock skew, implement token revocation and introspection for long-lived tokens, rotate keys, use mTLS between gateway and identity provider where possible, limit scopes and claims. - **Controls Applied:** JWT signature verification, Token introspection/revocation, Strict audience/issuer checks - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-004** - Application Services (Relational DB access) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** SQL injection or other injection vulnerabilities in APIs or query builders allowing modification of project/experiment metadata, deletion of records, or unauthorized access to other tenants' data. - **Mitigation Strategy:** Use parameterized queries/ORM with prepared statements, input validation and canonicalization, least-privilege DB accounts, database activity monitoring, schema checks, query whitelisting, and automated scanning for injection vulnerabilities. - **Controls Applied:** Parameterized queries, ORM usage, DB least-privilege accounts - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-005** - Realtime Collaboration & Notifications - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** WebSocket/real-time channels leaking sensitive project comments, chat content or data previews to unauthorized clients or other tenants because of missing per-connection auth, tenant checks, or improper channel scoping. - **Mitigation Strategy:** Authenticate and authorize each connection before accepting messages, include explicit tenant/project scope in messages and verify on server, use per-connection tokens (short-lived), encrypt channels (wss), and log/monitor message routing. - **Controls Applied:** Per-connection authentication, Tenant-scoped channel enforcement, WSS/TLS - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-006** - Data Storage (Object Storage) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly permissive ACLs result in datasets or model artifacts being publicly accessible, leading to IP loss or leakage of PII/training data. - **Mitigation Strategy:** Enforce deny-by-default bucket policies, block public ACLs, require bucket/object encryption at rest, use VPC endpoints for internal access, enable object-level logging and inventory scans, periodically audit permissions and use automated guardrails. - **Controls Applied:** Block public access policies, Server-side encryption, Access logging and inventory - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-007** - AI Assistant & LLM Orchestration - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Sensitive context (PII, secret keys, model IP) included in prompts or context windows is sent to external LLM providers and may be stored/used by them, causing data leakage or violation of tenant privacy preferences. - **Mitigation Strategy:** Redact or tokenize sensitive fields before sending to LLMs, allow tenant opt-out of third-party models, use private/self-hosted LLMs for sensitive tenants, encrypt prompts at rest, implement strict data usage policies and contractual DPAs with LLM vendors, and apply response filters to prevent leakage. - **Controls Applied:** Prompt redaction/tokenization, Tenant privacy opt-out, Private LLM options - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-008** - Integration & Deployment Services (CI/CD) - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Compromised CI/CD or deployment credentials are used to deploy malicious or backdoored models into staging/production, or to modify deployment configuration to elevate privileges of attacker code. - **Mitigation Strategy:** Use ephemeral credentials and workload identity (OIDC for workloads), enforce least-privilege service accounts, require signed/artifact provenance and manual approvals for production deploys, enforce image signing, and rotate CI/CD secrets frequently. - **Controls Applied:** Ephemeral credentials (OIDC), Artifact signing, Approval workflows - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-009** - Platform & Ops (Audit logs / Backups) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Insider or compromised admin modifies or deletes audit logs and backups to cover tracks after malicious actions (e.g., data exfiltration or privilege escalation). - **Mitigation Strategy:** Store append-only logs in immutable storage (WORM), replicate logs to independent accounts/providers, enforce separation of duties for log access, use KMS with restricted key use for logs, enable tamper-evident checksums and SIEM alerts. - **Controls Applied:** Append-only/immutable log storage, Separation of duties, External log replication - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-014** - Data Storage (Vector DB for embeddings) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Embeddings stored in vector DBs contain encoded PII or sensitive training data which can be probed or reconstructed by adversaries with query access, leaking private data across tenants. - **Mitigation Strategy:** Apply PII redaction before embedding, implement access controls and encryption at rest/in transit, implement query rate limits and ML privacy techniques (differential privacy, embeddings noise), segment per-tenant vector databases or use per-tenant encryption keys. - **Controls Applied:** PII redaction, Per-tenant encryption, Query rate limiting - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-015** - Model Registry & Deployment - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Attacker registers a malicious model artifact with forged metadata (faking provenance) then deploys it; users assume model is trustworthy and it performs malicious actions or leaks data. - **Mitigation Strategy:** Require model artifact signing and provenance metadata (attestation), enforce approval gates, automated static/dynamic analysis of model behavior (e.g., canary testing, sandbox inference inspection), restrict who can register or deploy, and maintain immutable model artifact storage. - **Controls Applied:** Artifact signing and attestation, Deployment approval workflows, Canary/sandbox testing - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-016** - Application Services (RBAC & Authorization) - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Broken access control or RBAC misconfiguration allows a user (e.g., Data Scientist or Viewer) to escalate privileges and perform admin actions (modify RBAC, change tenant settings, access other tenants' projects). - **Mitigation Strategy:** Enforce principle of least privilege, implement defense-in-depth authorization checks at service and data layer (not only UI), use automated policy testing, ABAC/attribute-based checks for sensitive operations, periodic access reviews and audits, and enforce separation of duties for tenant admin operations. - **Controls Applied:** Service-level authorization enforcement, Automated access policy testing, Periodic access reviews - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-019** - Edge / API Gateway / Frontend - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** TLS misconfiguration (weak ciphers, missing HSTS) or compromised certificates allowing MITM attacks that intercept tokens, API calls, or LLM prompts between users and gateway or between services. - **Mitigation Strategy:** Enforce strong TLS config, HSTS, certificate management with automated rotation and monitoring, use mTLS between internal services, certificate pinning for critical integrations, and continuous TLS scanning. - **Controls Applied:** Strong TLS/HSTS, mTLS internal communication, Automated certificate rotation - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-021** - External LLM Providers / AI Assistant - **Category:** Repudiation - **Likelihood:** High | **Impact:** Medium - **Initial Risk Level:** High - **Description:** Insufficient logging/auditing of prompts, LLM responses, and system decisions leads to inability to investigate incidents, attribute actions, or prove compliance when contested. - **Mitigation Strategy:** Record tamper-evident logs of prompts, responses, and decision metadata (including truncation/redaction flags), persist logs to immutable storage, include correlation IDs, and integrate with SIEM/EDR for alerting and long-term retention. - **Controls Applied:** Tamper-evident logging, Correlation IDs for AI calls, SIEM integration - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-022** - Frontend / Social OAuth flows - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** OAuth redirect_uri or client misconfiguration exploited to perform open redirect or token theft (redirect URI manipulation leading to code/token captured by attacker-controlled endpoint). - **Mitigation Strategy:** Strictly validate redirect_uris against registered list, require exact-match or use PKCE for public clients, implement CSRF/state validation, restrict allowed OAuth clients and monitor OAuth flows for anomalies. - **Controls Applied:** Strict redirect_uri validation, PKCE, OAuth state checks - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-023** - Data Management & Visualization (Dataset uploads) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Malicious or poisoned dataset uploads (either by an attacker or by a careless user) that later poison models, leading to biased or vulnerable models or deliberate backdoors. - **Mitigation Strategy:** Validate and scan uploaded datasets for anomalies and PII, track dataset provenance/lineage, require dataset approvals for production training, sandbox training runs for untrusted data, and maintain dataset versioning with rollbacks. - **Controls Applied:** Dataset scanning and validation, Provenance and approval workflows, Sandboxed training - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-024** - Integration & Deployment Services (CI/CD logs) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Secrets or service credentials accidentally exposed in CI/CD build logs or pipeline artifacts which are accessible to attackers or third-parties, enabling further compromise. - **Mitigation Strategy:** Use a centralized secrets manager, inject secrets at runtime (never store in code or logs), mask secrets in logs, scan pipelines for secrets, restrict pipeline artifact access, and use short-lived credentials and key rotation. - **Controls Applied:** Secrets manager integration, Log masking and scanning, Short-lived credentials - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-025** - Platform & Ops (KMS/Key management) - **Category:** Elevation of Privilege - **Likelihood:** Low | **Impact:** High - **Initial Risk Level:** High - **Description:** Compromise of KMS admin credentials or misuse of key policy allows an attacker to decrypt data-at-rest (datasets, model artifacts, embeddings), breaking confidentiality across tenants. - **Mitigation Strategy:** Use HSM-backed KMS, implement BYOK with tenant keys where feasible, split key management duties, enforce MFA and strong logging for key administration, restrict key usage with IAM policies, and rotate keys regularly. - **Controls Applied:** HSM-backed KMS, MFA for KMS admins, Key rotation and split duties - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-026** - Application Services / Data Storage (Multi-tenant DB) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Insufficient tenant isolation (shared DB schema without row-level security or incorrect tenant ID use) leads to cross-tenant data access where one tenant can read another tenant's projects, experiments or artifacts. - **Mitigation Strategy:** Implement explicit tenant context in all queries, apply row-level security or separate schemas/DBs per tenant, enforce strong access controls at service and DB layers, require tenant-scoped tokens, and include multi-tenant tests in CI. - **Controls Applied:** Row-level security or per-tenant DB separation, Tenant-scoped authorization checks - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-027** - Data Storage (Append-only Audit Logs) - **Category:** Repudiation - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Audit logs are alterable or deletable by privileged agents; attackers remove evidence of malicious activity or administrators deny actions leading to compliance failures. - **Mitigation Strategy:** Write audit logs to immutable storage and replicate to a separate cloud/account, sign logs, apply WORM policies, restrict direct log write/delete permissions to minimal roles, and integrate with external SIEM for tamper detection. - **Controls Applied:** Immutable/WORM logs, External log replication, Signed logs - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-028** - Frontend Layer / CDN - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Supply-chain attack or CDN compromise results in malicious JavaScript delivered to users (e.g., miner, credential siphon, content manipulator), affecting all tenants and allowing large-scale data exfiltration. - **Mitigation Strategy:** Use SRI for third-party scripts, minimize third-party dependencies, lock dependencies to verified versions, monitor CDN integrity, use signed releases and automated CI checks, and provide content integrity checks in the client. - **Controls Applied:** SRI, Signed frontend releases, Dependency pinning - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-029** - AI Assistant & LLM Orchestration - **Category:** Denial of Service - **Likelihood:** High | **Impact:** Medium - **Initial Risk Level:** High - **Description:** Abusive or scripted chat causing excessive LLM API calls, exhausting quotas or incurring high costs and causing degradation of AI assistant availability or high operational costs. - **Mitigation Strategy:** Implement per-user and per-tenant rate limits and quotas, require authentication for chat, cache common responses, add CAPTCHAs for suspicious flows, monitor cost telemetry, and provide fallbacks (cached suggestions) when limits are hit. - **Controls Applied:** Per-user/per-tenant quotas, Caching and throttling - **Control Effectiveness:** High - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review **THR-030** - External Services (External ML Platforms & Cloud Storage Connectors) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Initial Risk Level:** High - **Description:** Misconfigured external ML platform connectors or cloud storage integrations expose artifacts, inference payloads, or customer data (e.g., wrong permissions on remote buckets or mistakenly public model endpoints). - **Mitigation Strategy:** Enforce least-privilege connector credentials, validate external resource permissions upon connection, encrypt data in transit, periodically scan connected external resources for public exposure, and require tenant approval before storing data externally. - **Controls Applied:** Least-privilege connector credentials, External resource permission validation - **Control Effectiveness:** Medium - **Residual Risk Level:** Medium - **Status:** ⚠️ Requires Review ##### Medium Risk Threats **THR-010** - External Services (Identity Providers) - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Identity provider outage (OIDC/SAML provider failure or network partition) prevents users from authenticating, blocking access to the web application for SSO-enabled tenants. - **Mitigation Strategy:** Support multiple identity providers and fallback sign-in methods, cache recent tokens or sessions for short offline window, design graceful degradation (read-only access), monitor IDP health and enable alerting, and have emergency admin break-glass. - **Controls Applied:** IDP redundancy/fallback, Session caching for short windows - **Control Effectiveness:** Medium - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-012** - Frontend Layer / Application Services - **Category:** Tampering - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (project modification, model registration) from authenticated users without their knowledge. - **Mitigation Strategy:** Use anti-CSRF tokens or SameSite=strict cookies, double-submit cookie pattern, verify Origin/Referer for state-changing endpoints, and require re-authentication for high-risk actions. - **Controls Applied:** CSRF tokens / SameSite cookies, Origin checking for state changes - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-017** - AI Assistant & Frontend (Chat history) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Chat history export feature or saved transcripts accessible without proper authorization, allowing users to download other users' chat logs containing sensitive project context or PII. - **Mitigation Strategy:** Enforce per-user/tenant authorization on export endpoints, require confirmation and audit logging for exports, rate-limit exports, sanitize transcripts (redact secrets), and allow users to opt-out of persistent chat storage. - **Controls Applied:** Authorization checks on export, Export logging and audit - **Control Effectiveness:** Medium - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-018** - Data Storage (Time-series DB for metrics) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Attacker manipulates time-series metrics (model accuracy, drift stats) to hide model degradation or to spoof performance metrics, leading to incorrect deployment decisions and harm to ML lifecycle integrity. - **Mitigation Strategy:** Authenticate and authorize metric ingestion endpoints, sign metrics at source where possible, maintain immutable metric snapshots, detect anomalies with SIEM, and replicate metrics to separate storage for integrity verification. - **Controls Applied:** Authenticated ingestion endpoints, Immutable snapshots/replication, Anomaly detection - **Control Effectiveness:** Medium - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **THR-020** - Realtime Collaboration & Notifications - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** Medium - **Initial Risk Level:** Medium - **Description:** Resource exhaustion through many persistent websocket connections, presence events, or push notifications from malicious clients causing service degradation for collaboration features. - **Mitigation Strategy:** Authenticate before resource allocation, enforce connection limits per user/tenant/IP, rate-limit presence events and messages, use autoscaling and resource quotas, and implement connection blacklisting and throttling. - **Controls Applied:** Connection limits and throttles, Auth before allocation - **Control Effectiveness:** High - **Residual Risk Level:** Low - **Status:** ⚠️ Requires Review **Risk Reduction Summary:** - **Critical Risk Reduction:** 4 threats reduced from Critical to lower levels - **High Risk Reduction:** 21 threats reduced from High to lower levels - **Residual Risk Distribution:** 2 threats remain at Critical/High level ### 5.3. Risk Summary Critical and high risks center on identity and session protection, LLM-related data leakage and prompt-injection, API gateway availability and abuse, multi-tenant isolation failures, and integrity of artifacts/logs. Immediate attention should focus on: 1) Authentication hardening (2FA, PKCE, token validation) and token/session protection; 2) Secure LLM orchestration (prompt redaction, response filters, private LLM options, audit logs) to avoid sensitive data exfiltration and prompt injection; 3) API gateway protections (rate limiting, WAF, bot detection) to prevent DoS and abuse of expensive endpoints; 4) Multi-tenant data isolation (row-level security or per-tenant DBs, per-tenant encryption); 5) Artifact provenance and pipeline secrets protection (artifact signing, ephemeral CI credentials, secrets management). Key attack vectors are: compromised or forged identity tokens, XSS/supply-chain on the frontend, malicious or crafted prompts to LLMs, misconfigured external storage/M L connectors, and compromised CI/CD/KMS credentials. The overall posture currently shows multiple High/Critical threats that can be significantly reduced by implementing recommended mitigations: robust auth and token handling, strong input/output controls for the frontend and AI assistant, immutable logging and separation of duties for ops, and enforcement of per-tenant controls and encryption. Prioritize controls in this order: identity & session security, API gateway & rate-limiting, LLM prompt/data controls, tenant isolation & encryption, CI/CD and key management. Ongoing controls should include automated scanning, threat-injection testing (red-team/pen tests), continuous monitoring/SIEM alerts for anomalous access patterns, and strict operational procedures for keys and logs to reduce residual risk. --- ## 6. Multi-Standard Security Requirements Mapping This section maps each functional requirement to specific security controls from multiple industry standards: OWASP Application Security Verification Standard (ASVS), NIST SP 800-53 Rev 5, and ISO 27001:2022. This multi-standard approach provides comprehensive coverage across application-level, enterprise-level, and organizational-level security domains: - **OWASP ASVS**: Application-level security controls (code, APIs, authentication, session management) - **NIST SP 800-53**: Enterprise security controls (governance, risk management, incident response) - **ISO 27001**: Information security management controls (policies, procedures, organizational controls) Requirements are prioritized based on risk assessment and compliance needs, with controls selected from the most appropriate standard(s) for each requirement type. ### 6.1. Recommended ASVS Compliance Level **Recommended Level:** L2 **Level 2: Standard** Recommended for most production applications. Provides comprehensive security coverage suitable for applications handling sensitive data or operating in regulated environments. Includes controls for authentication, authorization, data protection, and secure communications. The recommendation considers factors such as: - Data sensitivity and classification levels - Regulatory and compliance requirements (GDPR, HIPAA, PCI-DSS, etc.) - Threat landscape and risk assessment from threat modeling - Business criticality and potential impact of security incidents All security controls referenced in this document align with this recommended compliance level. ### 6.2. Requirements Mapping This section maps each high-level requirement to specific security controls from multiple standards (OWASP ASVS, NIST SP 800-53, ISO 27001) with detailed descriptions, relevance explanations, and integration guidance. Controls are grouped by standard for clarity. #### 6.2.1. REQ-001: User registration and login with email/password and social OAuth (Google, GitHub) ##### OWASP ASVS Controls **ASVS 2.1** **Requirement:** Verify all authentication functions require a username and password, and sessions are established using secure session tokens. Passwords are validated using secure hashing and salting algorithms. **Relevance:** Directly applies to email/password registration and login; ensures session tokens and password storage use secure methods. It sets baseline authentication security requirements for any user login functionality. **Integration Tips:** Use strong hashing (e.g., bcrypt/argon2) with per-user salts and enforce secure session tokens with proper attributes (HttpOnly, Secure, SameSite). Ensure registration flows validate inputs and do not leak account existence via timing/errors. **Verification Method:** Review password hashing configurations, perform code inspection of authentication code, and run authentication flow tests including session token attribute checks. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **IA-5** **Requirement:** Authenticator Management: Organizations manage information system authenticators by establishing, issuing, and revoking authenticators such as passwords and tokens. **Relevance:** Covers lifecycle of authenticators for email/password and OAuth tokens; ensures proper issuance and revocation processes exist. Important for handling account compromises and token revocation for social logins. **Integration Tips:** Implement centralized authenticator lifecycle management including password reset, rotation, and revocation for OAuth tokens. Integrate with identity provider APIs to revoke tokens and maintain revocation logs. **Verification Method:** Inspect authenticator management procedures, test token revocation flows with providers, and review logs showing issuance and revocation events. **Priority:** Critical ##### ISO 27001:2022 Controls **A.9.2.3** **Requirement:** User access provisioning should be implemented based on business and information security requirements. Access rights shall be reviewed regularly. **Relevance:** Ensures that user accounts created via registration are provisioned with appropriate rights and subject to periodic review. Applies to initial account creation and social/OAuth-linked accounts. **Integration Tips:** Implement user provisioning workflows that assign minimal default privileges and include periodic access reviews. Link account lifecycle events to provisioning records for auditability. **Verification Method:** Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews. **Priority:** High #### 6.2.2. REQ-002: Single Sign-On (SSO) via OIDC and SAML for enterprise customers ##### OWASP ASVS Controls **ASVS 2.6** **Requirement:** Applications that support federated authentication must validate identity tokens using the provider's public keys and verify all token claims and audiences. **Relevance:** Direct requirement for OIDC/SAML validation; enforces cryptographic validation of tokens and checking of claims to prevent impersonation. Essential for secure SSO. **Integration Tips:** Use well-maintained libraries to validate ID tokens and SAML assertions, fetch and cache provider public keys (JWKS), and verify audience, issuer, expiry, and nonce parameters. **Verification Method:** Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **IA-2(1)** **Requirement:** Use of SAML and other federated identity attributes for authentication must ensure assertions are validated and replay-protected. **Relevance:** Specifies validation and replay protection for SAML/OIDC assertions which is directly applicable to SSO. Prevents assertion replay and misuse of federated credentials. **Integration Tips:** Implement audience restriction, timestamp checks, one-time-use/nonces, and verify assertion freshness. Employ secure channels for metadata exchange and rotate keys as necessary. **Verification Method:** Review SAML/OIDC library configurations, test replay attempts, and verify assertion validation logic and timestamps. **Priority:** Critical ##### ISO 27001:2022 Controls **A.9.4.2** **Requirement:** Where required, secure log-on procedures shall be implemented, and authentication mechanisms should support federated identity when used by the organization. **Relevance:** Provides organizational control requiring secure log-on and allowance for federated identity; aligns policy with SSO implementation. Ensures SSO is integrated into secure access policies. **Integration Tips:** Update access control policies to include federation; document how SSO providers are approved and how authentication is logged. Ensure training and procedures for SSO onboarding. **Verification Method:** Review policy documents, SSO onboarding records, and evidence that secure log-on procedures are followed (logs, configurations). **Priority:** High #### 6.2.3. REQ-003: Role-based access control (Admin, Project Manager, Data Scientist, Viewer) and team workspaces ##### OWASP ASVS Controls **ASVS 5.1** **Requirement:** Verify that role-based access control enforces least privilege and separation of duties; authorization checks must be enforced server-side. **Relevance:** Directly applicable to implementing RBAC and enforcing server-side checks for roles like Admin and Data Scientist in team contexts. Prevents client-side bypass. **Integration Tips:** Design RBAC with clearly defined roles and permissions, implement centralized server-side policy enforcement, and model team workspace scoping for resource access. **Verification Method:** Access control tests (attempt privilege escalation), code review for server-side enforcement, and role permission matrices verification. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-2** **Requirement:** Account Management: The organization manages system accounts, including owner, group, and role assignments and privileges. **Relevance:** Describes account and role management processes required to implement RBAC and team workspaces, ensuring accounts and roles are managed consistently. **Integration Tips:** Define role lifecycle procedures (creation, modification, removal), standardize role definitions, and integrate with identity management for teams and groups. **Verification Method:** Review account management procedures, audit role assignment logs, and sample checks of group/role membership. **Priority:** High ##### ISO 27001:2022 Controls **A.9.1.2** **Requirement:** Access to systems and services should be controlled by access control mechanisms and role definitions appropriate to user responsibilities. **Relevance:** Reinforces requirement to restrict access according to roles and responsibilities; applicable to team workspace access and segregation. **Integration Tips:** Map business roles to system roles, document access matrices, and include role-based controls in access control policy. Conduct periodic access reviews. **Verification Method:** Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions. **Priority:** High #### 6.2.4. REQ-004: User profile management with avatar uploads, preferences, and 2FA support ##### OWASP ASVS Controls **ASVS 4.0** **Requirement:** Applications must support multi-factor authentication for sensitive functions and provide secure mechanisms for managing user profile data. **Relevance:** Directly applicable to adding 2FA to profiles and ensuring profile data management is secure. Emphasizes MFA for sensitive actions and proper handling of profile data. **Integration Tips:** Offer TOTP/WebAuthn options for 2FA, protect profile endpoints with strong authentication, and require re-authentication for sensitive profile changes. Store preferences securely. **Verification Method:** Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **IA-2** **Requirement:** Identification and authentication (organizational users) – The system uniquely identifies and authenticates organizational users; multi-factor authentication is required for high-risk operations. **Relevance:** Defines MFA requirements and unique identification which directly apply to profile management and sensitive preferences or avatar uploads that could introduce risk. **Integration Tips:** Apply MFA for high-risk profile operations (credential changes, sensitive preferences) and ensure user ID uniqueness and secure authentication storage for recovery processes. **Verification Method:** Review MFA usage logs, perform attempts to bypass MFA, and validate uniqueness checks in authentication flows. **Priority:** High ##### ISO 27001:2022 Controls **A.9.4.1** **Requirement:** Access to information and application functions should be restricted and managed, including user profile information; media and file handling considerations apply to uploads. **Relevance:** Covers restrictions on profile data and file uploads such as avatars; highlights need to manage and restrict access to uploaded media. **Integration Tips:** Implement file upload restrictions (type/size), store files on segregated storage with restricted permissions, and apply virus scanning and metadata stripping. **Verification Method:** Inspect file handling code, test upload vectors, and verify access controls to stored avatars and profile data. **Priority:** High #### 6.2.5. REQ-005: Create and organize ML projects with metadata, tags, and access controls ##### OWASP ASVS Controls **ASVS 5.7** **Requirement:** Metadata and object-level access control must be enforced; access control policies should consider object attributes like tags and ownership. **Relevance:** Directly relevant for project and metadata-level access controls so that tagging and metadata do not bypass authorization. **Integration Tips:** Implement attribute-based access control (ABAC) where tags/metadata influence policy evaluation; ensure server-side enforcement of object-level permissions. **Verification Method:** Test access to objects with different tag/ownership combinations and review policy evaluation logs. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-3** **Requirement:** Access Enforcement: The information system enforces approved authorizations for logical access to information and system resources in accordance with applicable access control policies. **Relevance:** Ensures that access control policies for projects and metadata are enforced consistently across the system. **Integration Tips:** Centralize enforcement points for project resources, ensure policies are well-defined and consistently applied across APIs and UI, and log enforcement decisions. **Verification Method:** Review enforcement points, run policy conformance tests, and inspect authorization logs for policy application. **Priority:** High ##### ISO 27001:2022 Controls **A.8.1.1** **Requirement:** Assets associated with information and information processing facilities should be identified and an inventory maintained, including classification and ownership. **Relevance:** Project and dataset artifacts are information assets; identifying owners and classification supports correct access controls and handling. **Integration Tips:** Maintain an inventory of projects and assigned owners; classify projects by sensitivity to drive access control rules and retention policies. **Verification Method:** Inspect the asset inventory and mapping of owners/classifications to access controls. **Priority:** Medium #### 6.2.6. REQ-006: Experiment tracking with versioning, metrics, artifacts, visualizations and comparison UI ##### OWASP ASVS Controls **ASVS 10.3** **Requirement:** Protect data integrity and provide mechanisms for versioning and tamper-evidence of critical artifacts and datasets. **Relevance:** Addresses need for integrity and tamper-evidence for experiment artifacts and metrics to ensure trustworthiness of tracked experiments. **Integration Tips:** Use content-addressable storage or checksums to detect tampering, sign critical artifacts, and maintain immutable versioned histories for artifacts and metrics. **Verification Method:** Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **CM-3** **Requirement:** Configuration Change Control: The organization develops, documents, and maintains baselines and tracks changes to system components and artifacts. **Relevance:** Applies to tracking experiment configurations and versions as part of configuration management to ensure traceability and controlled changes. **Integration Tips:** Maintain baselines for experiments and configurations, track changes with metadata (who, when, why), and integrate change control into comparison UI. **Verification Method:** Review change tracking records, test ability to view diffs between versions, and confirm baselines exist for experiments. **Priority:** High ##### ISO 27001:2022 Controls **A.12.5.1** **Requirement:** Changes to information processing facilities and systems should be controlled to prevent unauthorized changes. **Relevance:** Ensures experiment changes and artifact updates go through controlled change processes to avoid unauthorized modifications. **Integration Tips:** Require approvals for production-affecting experiment runs, log changes to experiment metadata, and integrate change tickets with experiment promotions. **Verification Method:** Review change management tickets tied to experiments and audit logs for unauthorized modifications. **Priority:** Medium #### 6.2.7. REQ-007: Save and share experiment configurations as templates and project-level sharing settings ##### OWASP ASVS Controls **ASVS 5.9** **Requirement:** Ensure that sharing and template features respect authorization policies; ability to share must be constrained by role and ownership rules. **Relevance:** Directly mandates that sharing templates must honor authorization and not allow privilege escalation or data leakage via templates. **Integration Tips:** Implement checks on template creation and sharing that validate sender and recipient permissions; include audit trails for shares and enforce least privilege. **Verification Method:** Test sharing scenarios across roles, attempt unauthorized shares, and inspect share audit logs. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **AC-6** **Requirement:** Least Privilege: The organization ensures that users are granted the least privileges necessary to accomplish their tasks, including sharing capabilities. **Relevance:** Ensures sharing is limited to necessary privileges; templates should not grant more access than intended. **Integration Tips:** Design sharing permissions with role-based constraints and require owners/admin approval for broader sharing; provide granular share scopes. **Verification Method:** Review permission assignments, simulate least privilege violations, and check enforcement logs. **Priority:** Medium ##### ISO 27001:2022 Controls **A.13.2.1** **Requirement:** Information transfer policies should include controls for sharing and distribution of information within and outside the organization. **Relevance:** Applicable to templates and project-level sharing policies to ensure sharing respects organization policies and external transfer controls. **Integration Tips:** Create policies for internal/external template sharing, require explicit opt-in for external shares, and log transfers and consent. **Verification Method:** Review information transfer policies and check logs for adherence to procedures during sharing operations. **Priority:** Medium #### 6.2.8. REQ-008: Activity feed and audit logs for project and system changes ##### OWASP ASVS Controls **ASVS 11.1** **Requirement:** Application must generate logs for security-relevant events and ensure logs cannot be tampered with; include user actions, admin actions and data changes. **Relevance:** Directly mandates logging of user and admin actions relevant to activity feeds and audit trails to support accountability and forensics. **Integration Tips:** Log critical events with non-repudiation mechanisms (append-only storage, HMAC) and separate privileges for log access; include contextual metadata (user, project, action). **Verification Method:** Inspect logs for required events, attempt tampering to validate protections, and verify retention/rotation policies. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AU-2** **Requirement:** Audit Events: The information system must determine and record auditable events and provide a record of actions taken. **Relevance:** Specifies which events to audit and records to maintain, directly applicable to activity feed and audit logs design. **Integration Tips:** Define auditable events per system components (project changes, permissions changes), ensure timestamps and actor IDs are recorded, and protect audit records. **Verification Method:** Review audit event definitions, inspect sample logs, and validate completeness against event list. **Priority:** Critical ##### ISO 27001:2022 Controls **A.12.4.1** **Requirement:** Event logs recording user activities, exceptions, and information security events should be produced and retained. **Relevance:** Reinforces need for event logging and retention policies covering system and project changes for compliance and incident response. **Integration Tips:** Define retention periods, secure log storage, and access controls for log review. Integrate logs into SIEM for monitoring and alerting. **Verification Method:** Check log retention configurations, review access controls to logs, and confirm logs include required event types. **Priority:** High #### 6.2.9. REQ-009: Conversational AI assistant (LLM) with chat UI, multi-language support and saved chat history ##### OWASP ASVS Controls **ASVS 10.1** **Requirement:** Sensitive data processed by application features like chat should be classified and protected; stored chat history must be access-controlled and encrypted. **Relevance:** Directly relevant to protecting chat transcripts and LLM interactions which may include PII or sensitive project context. **Integration Tips:** Encrypt chat histories at rest, apply access controls based on roles and project context, classify content and redact or mask sensitive items prior to storage. **Verification Method:** Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **PL-8** **Requirement:** Information Security Architecture: Ensure that system design includes protections for privacy and confidentiality of user-generated content and retains accountability. **Relevance:** Requires architectural consideration for LLM components to protect privacy and ensure accountability for generated/stored content. **Integration Tips:** Design the LLM integration with privacy-by-design principles, include data minimization, and log model inputs/outputs for accountability while protecting sensitive items. **Verification Method:** Review architecture diagrams, validate data flows, and confirm presence of privacy controls and logging/monitoring for LLM services. **Priority:** High ##### ISO 27001:2022 Controls **A.18.1.4** **Requirement:** Personal data shall be protected in accordance with relevant legislation and organizational policies, including chat transcripts. **Relevance:** Ensures chat history and personal data processed by the assistant are protected in line with legal/regulatory obligations. **Integration Tips:** Implement user consent mechanisms, define retention for chat data, and provide controls for export/deletion to meet data subject rights. **Verification Method:** Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts. **Priority:** High #### 6.2.10. REQ-010: Natural-language search across experiments, models, datasets and chat history ##### OWASP ASVS Controls **ASVS 9.3** **Requirement:** Search functionality must enforce access control checks on results and avoid information leakage via search suggestions or metadata. **Relevance:** Search across sensitive artifacts must not return unauthorized results; prevents leakage via NLP search suggestions or indexed metadata. **Integration Tips:** Ensure search index enforces row-level security, filter results by user/project permissions, and sanitize suggestion engines to avoid exposing counts or metadata of inaccessible items. **Verification Method:** Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-19** **Requirement:** Access Control for Mobile Devices and Remote Access: Limitations and protections on information retrieval mechanisms must be enforced to prevent unauthorized disclosure. **Relevance:** Mandates protective controls for remote search and retrieval mechanisms to prevent unauthorized disclosure; applies to natural-language search. **Integration Tips:** Apply authentication/authorization on search APIs, rate-limit queries to mitigate enumeration, and log search access for auditing. **Verification Method:** Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns. **Priority:** High ##### ISO 27001:2022 Controls **A.9.2.5** **Requirement:** Access rights should be reviewed to ensure that search and retrieval functions do not expose data beyond authorized users. **Relevance:** Supports ongoing verification that search permissions align with assigned access rights and prevent inadvertent exposure. **Integration Tips:** Include search access checks in periodic access reviews and adjust index/build processes when access rights change. **Verification Method:** Check results of access reviews, and validate that changes to permissions update search index behavior. **Priority:** Medium #### 6.2.11. REQ-011: AI-powered code suggestions, explanations, experiment summaries, and automated insights ##### OWASP ASVS Controls **ASVS 10.4** **Requirement:** AI-generated outputs that may expose sensitive information must be sanitized and the model prompts/outputs should be logged and controlled for confidentiality. **Relevance:** Directly relevant: AI outputs can leak secrets or IP; this control mandates sanitization and control over model prompts/outputs to prevent leakage. **Integration Tips:** Implement output filters that detect and redact secrets, log prompts/outputs for review with access controls, and apply model input sanitization to avoid leaking sensitive content. **Verification Method:** Review logs to ensure prompt/output recording and filtering, test the system by feeding sensitive prompts and verifying redaction. **Level:** L3 | **Priority:** Critical ##### NIST SP 800-53 Controls **SI-11** **Requirement:** Error Handling and Information Leakage: Applications must detect and protect against unauthorized dissemination of information via outputs and generated content. **Relevance:** Requires detection and prevention of output-based leakage, applicable when code suggestions or summaries could reveal sensitive or proprietary data. **Integration Tips:** Add detectors for confidential patterns (API keys, secrets) in AI outputs, restrict model capabilities for certain projects, and implement human review for high-risk outputs. **Verification Method:** Conduct tests that confirm detection of secret patterns, review human-in-the-loop approvals, and audit output logs for leakage incidents. **Priority:** High ##### ISO 27001:2022 Controls **A.9.4.4** **Requirement:** Controls should be applied to tools and utilities that can access sensitive information, including AI tools that generate or transform data. **Relevance:** Ensures AI tooling that can access or generate sensitive content is treated like privileged utilities and controlled accordingly. **Integration Tips:** Restrict AI tool access with least privilege, log usage by identity/project, and require approvals for access to datasets or models that produce sensitive outputs. **Verification Method:** Review access controls for AI tooling, usage logs, and approval workflows for privileged AI features. **Priority:** Medium #### 6.2.12. REQ-012: Context-aware help respecting current project context and user role ##### OWASP ASVS Controls **ASVS 5.2** **Requirement:** Contextual features must honor authorization policies and ensure that help or guidance does not reveal unauthorized data or operations. **Relevance:** Context-aware help must not reveal information outside a user's role; enforces that help respects authorization boundaries. **Integration Tips:** Ensure help content generation queries use the same authorization layer as UI views; mask or suppress details the user is not authorized to see. **Verification Method:** Test help outputs in various roles/projects and confirm no unauthorized data is revealed. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **AC-6(1)** **Requirement:** Least Privilege Enforcement: Systems that provide context-based capabilities must apply least privilege to contextual data and actions. **Relevance:** Reinforces applying least privilege to contextual help and preventing exposure of higher-privilege details through help features. **Integration Tips:** Implement scoping of contextual help queries to the user's effective permissions and avoid aggregating information across projects unless permitted. **Verification Method:** Perform privilege escalation tests using contextual help and log analysis to ensure scoping is honored. **Priority:** Medium ##### ISO 27001:2022 Controls **A.7.1.2** **Requirement:** Users must be made aware of their security responsibilities; context-aware help should align with these policies. **Relevance:** Ensures help does not contradict organizational security policies and that user-facing guidance reinforces security responsibilities. **Integration Tips:** Align help text with security policies and include reminders of responsibilities in context-aware tips, especially for privileged actions. **Verification Method:** Review help content against policy and gather user feedback on compliance alignment. **Priority:** Low #### 6.2.13. REQ-013: Chat export and per-user chat history controls (export/delete) ##### OWASP ASVS Controls **ASVS 10.6** **Requirement:** Provide mechanisms for users to export their data and request deletion; systems must verify authenticity of requests and ensure propagation. **Relevance:** Directly applies to chat export and deletion flows, enforcing verification and safe execution of these operations. **Integration Tips:** Implement authenticated export endpoints with rate limits and data packaging, and ensure deletion routines remove data from primary and backup stores where required. **Verification Method:** Test export and deletion flows end-to-end including backups, and verify authenticity checks and propagation to dependent systems. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **MP-6** **Requirement:** Media Sanitization: Ensure data is properly removed from storage when deletion/export requests are fulfilled. **Relevance:** Ensures deleted chat history is sanitized from all media/storage mediums and not left recoverable on backups or caches. **Integration Tips:** Design deletion to mark records for sanitization and run secure deletion or cryptographic erasure where possible; track deletion tasks to completion. **Verification Method:** Inspect sanitization procedures, attempt to recover deleted chat artifacts from backups, and verify logs of completed sanitization. **Priority:** High ##### ISO 27001:2022 Controls **A.18.1.4** **Requirement:** Implement procedures to support privacy rights, including data access, export, and deletion consistent with legislation. **Relevance:** Mandates support for data subject rights including export and deletion applicable to chat histories and per-user controls. **Integration Tips:** Provide transparent processes and timelines for export/delete, authenticate requestors, and document compliance with legal obligations. **Verification Method:** Review data subject request procedures, test sample export-delete requests, and validate audit trail of actions. **Priority:** High #### 6.2.14. REQ-014: Dataset upload, preview (tables/images/structured data), and data versioning/lineage ##### OWASP ASVS Controls **ASVS 8.5** **Requirement:** File uploads must be validated, stored securely, scanned for malware, and access-controlled; previews should not expose underlying storage metadata. **Relevance:** Directly applies to dataset upload and preview functionality to prevent malware, exfiltration or leakage via previews. **Integration Tips:** Validate file types and sizes, run malware scanning and content inspection, strip metadata before preview, and store uploads in isolated, permissioned buckets. **Verification Method:** Conduct upload fuzzing, verify malware scanning logs, and test metadata stripping on previews. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SI-3** **Requirement:** Malicious Code Protection: The information system must employ mechanisms to detect and prevent malicious code from uploaded content. **Relevance:** Covers preventing malicious content in uploaded datasets which is critical to protect processing and preview systems. **Integration Tips:** Integrate AV and sandboxing for uploaded files, apply content-type validation, and restrict processing of untrusted file types. **Verification Method:** Review AV/sandbox integration logs and simulate uploads of malicious artifacts to verify protection. **Priority:** High ##### ISO 27001:2022 Controls **A.12.3.1** **Requirement:** Backup and versioning processes should be implemented to ensure data integrity and availability; provenance and lineage should be traceable. **Relevance:** Supports data versioning/lineage and ensures datasets are backed up and provenance recorded for traceability and recovery. **Integration Tips:** Implement versioned storage for datasets, capture lineage metadata on ingest, and ensure backups include provenance metadata while respecting retention policies. **Verification Method:** Review versioning strategy, lineage metadata records, and backup integrity checks. **Priority:** Medium #### 6.2.15. REQ-015: Interactive metric visualization and drag-and-drop custom dashboards with export (image/PDF) ##### OWASP ASVS Controls **ASVS 9.1** **Requirement:** Ensure all data displayed in dashboards is properly encoded and rendered to prevent XSS and injection vulnerabilities. **Relevance:** Dashboards that render user-supplied or dataset-derived content are susceptible to XSS; encoding is essential for secure visualization. **Integration Tips:** Apply context-sensitive output encoding for HTML, SVG, and JavaScript contexts, sanitize user-supplied widgets, and restrict allowed widget scripts. **Verification Method:** Run XSS tests on dashboard inputs/exports and perform code review of rendering pipelines. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SC-7** **Requirement:** Boundary Protection: Ensure secure processing and rendering boundaries to avoid unauthorized data leakage during export processes. **Relevance:** Ensures exports (PDF/image) do not leak hidden metadata or unintentional content; boundary protections prevent cross-tenant leakage. **Integration Tips:** Render exports in isolated environments, strip metadata, and validate export content against user permissions before delivery. **Verification Method:** Inspect exported files for metadata, test cross-tenant export attempts, and validate isolation mechanisms. **Priority:** High ##### ISO 27001:2022 Controls **A.14.2.8** **Requirement:** Applications and systems providing visualization must be tested for security vulnerabilities as part of development lifecycle. **Relevance:** Mandates testing of dashboard features for security vulnerabilities to prevent exploitation in visualization/export features. **Integration Tips:** Include security tests in CI for dashboard components, such as SAST/DAST for rendering engines, and perform export content reviews. **Verification Method:** Review security test results, CI pipelines, and results of vulnerability scans for dashboard modules. **Priority:** Medium #### 6.2.16. REQ-016: Model registry with metadata, versioning, comparison, and model card generation ##### OWASP ASVS Controls **ASVS 10.3** **Requirement:** Maintain integrity and provenance of models and associated metadata; model artifacts should be versioned and access-controlled. **Relevance:** Directly relevant for model registry to ensure integrity, provenance, and access control are enforced for models and their metadata. **Integration Tips:** Use signed artifacts, maintain immutable model versions, record provenance metadata (training data, hyperparameters), and protect model storage with ACLs. **Verification Method:** Verify artifact signatures, examine provenance metadata, and test access control enforcement on model artifacts. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **CM-8** **Requirement:** Information System Component Inventory: The organization tracks and documents components including models and their versions. **Relevance:** Requires inventory and documentation of models, which supports registry capabilities and tracking of model versions and components. **Integration Tips:** Maintain an authoritative registry listing models, versions, owners, and dependencies; integrate inventory updates with CI/CD for model promotion. **Verification Method:** Review model inventory, sampling to confirm metadata accuracy, and check synchronization with deployment pipelines. **Priority:** High ##### ISO 27001:2022 Controls **A.8.2.1** **Requirement:** Information assets including models should be classified and protected according to sensitivity and business value. **Relevance:** Model cards and registry metadata should be classified to inform protection levels for models (e.g., proprietary vs public). **Integration Tips:** Classify models and apply appropriate access controls and handling rules; include classification in model cards and registry metadata. **Verification Method:** Review classification labels in registry, confirm enforcement of handling rules, and audit access to classified models. **Priority:** Medium #### 6.2.17. REQ-017: Deploy models to staging and production via UI and support A/B testing and rolling deployments ##### OWASP ASVS Controls **ASVS 14.1** **Requirement:** Deployment pipelines must be secured, use signed artifacts, and support rollback; environment segregation should prevent accidental promotion of unapproved artifacts. **Relevance:** Ensures secure deployment processes for models with signatures and environment segregation to prevent unauthorized promotion to production. **Integration Tips:** Require artifact signing and verification during deployments, implement separate credentials for staging vs production, and provide safe rollback mechanisms. **Verification Method:** Review pipeline configs for signature verification, test rollback and promotion flows, and inspect environment segregation. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SA-9** **Requirement:** External System Services: Ensure security considerations for deployment and operational procedures including CI/CD and testing environments. **Relevance:** Advises on inclusion of security in acquisition and operation of deployment tooling, relevant for A/B testing and rolling deployments. **Integration Tips:** Document security requirements for deployment systems, ensure CI/CD credentials are secured and rotate secrets, and isolate A/B testing environments. **Verification Method:** Review CI/CD security settings, secret storage, and evidence of security requirements in procurement or configuration. **Priority:** High ##### ISO 27001:2022 Controls **A.12.1.2** **Requirement:** Changes to systems, including deployments, must follow change management procedures and be authorized prior to implementation. **Relevance:** Ensures deployments (model promotions, A/B rollouts) follow approved change management to avoid accidental or unauthorized production changes. **Integration Tips:** Integrate deployment UI actions with change approval workflows and retain audit trails for deployment approvals and rollouts. **Verification Method:** Inspect change tickets linked to deployments and verify approvals/logs for production promotions. **Priority:** High #### 6.2.18. REQ-018: Model monitoring dashboard (accuracy, drift, latency) and alerting ##### OWASP ASVS Controls **ASVS 11.3** **Requirement:** Systems should monitor operational metrics and security-relevant metrics and generate alerts for anomalous behavior, including model drift indicators. **Relevance:** Directly applicable for model monitoring to detect drift or anomalous performance which could indicate data or model issues. **Integration Tips:** Instrument model telemetry, define thresholds for alerts, route alerts to incident management, and ensure telemetry is access-controlled and tamper-resistant. **Verification Method:** Review telemetry collection, simulate drift conditions and verify alerting, and inspect alert handling records. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **SI-4** **Requirement:** Information System Monitoring: The system must detect and report security-relevant events and anomalies. **Relevance:** Mandates monitoring and detection which align with model monitoring dashboards to capture anomalies and incidents. **Integration Tips:** Integrate monitoring outputs with SIEM/incident systems, ensure metrics are correlated with security events, and maintain monitoring coverage for key performance indicators. **Verification Method:** Check monitoring coverage, correlation rules, and incident logs for model-related alerts. **Priority:** High ##### ISO 27001:2022 Controls **A.16.1.1** **Requirement:** Responsibilities and procedures for incident detection and reporting should be established, including monitoring systems for service performance and security incidents. **Relevance:** Ensures there are procedures for responding to monitoring alerts (e.g., model drift) as part of incident management. **Integration Tips:** Define owner responsibilities for monitoring alerts, document response playbooks for model issues, and train on escalation paths. **Verification Method:** Review incident response plans and perform tabletop exercises for model-monitoring incidents. **Priority:** Medium #### 6.2.19. REQ-019: Commenting, threaded discussions, @mentions and real-time collaboration on projects ##### OWASP ASVS Controls **ASVS 9.4** **Requirement:** User-generated content must be sanitized and encoded to prevent XSS; real-time components must protect against injection and CSRF. **Relevance:** Applies to comments/threaded content and @mentions to prevent injection attacks and cross-site scripting in collaboration features. **Integration Tips:** Sanitize and encode messages for display contexts, enforce CSRF protections on actions, and limit allowed markup or provide a safe subset for rich text. **Verification Method:** Perform XSS and injection testing on commenting and mention features and review sanitization libraries used. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SC-13** **Requirement:** Cryptographic Protection: Communications and collaboration channels should be protected to ensure confidentiality and integrity. **Relevance:** Real-time collaboration requires protection of message channels to prevent eavesdropping or tampering, ensuring integrity of threaded discussions. **Integration Tips:** Use TLS for transport, apply end-to-end protections where feasible, and authenticate message sources to prevent impersonation via mentions. **Verification Method:** Inspect TLS configurations, review message authentication mechanisms, and verify integrity protections. **Priority:** High ##### ISO 27001:2022 Controls **A.6.2.1** **Requirement:** Policies for use of collaboration tools and acceptable use should be defined and enforced. **Relevance:** Ensures policies governing use of collaboration tools are in place and applied to protect project discussions and collaboration. **Integration Tips:** Define acceptable use policies for collaboration, include guidance on sharing sensitive information in comments, and enforce via training and technical controls. **Verification Method:** Review policies and conduct audits of collaboration use for policy compliance. **Priority:** Low #### 6.2.20. REQ-020: Public project showcase with opt-in sharing controls ##### OWASP ASVS Controls **ASVS 5.9.2** **Requirement:** Publishing or sharing features must be explicit (opt-in) and enforce authorization checks; privacy controls should prevent accidental exposure. **Relevance:** Directly enforces opt-in sharing and authorization checks required for public showcases to prevent accidental publicity. **Integration Tips:** Require explicit consent/opt-in UI for public projects, maintain an audit of opt-ins, and provide an easy revoke/unpublish flow that propagates to caches/search indexes. **Verification Method:** Test publish/unpublish flows, check audit logs for opt-in actions, and verify propagation to public endpoints. **Level:** L2 | **Priority:** High ##### NIST SP 800-53 Controls **PL-4** **Requirement:** Rules of Behavior: Organizations must define user responsibilities for sharing information and explicit user consent when making data public. **Relevance:** Requires defining responsibilities and consent which applies to users publishing public projects. **Integration Tips:** Document rules of behavior for public sharing, require explicit consent flows, and provide training for users about public exposure risks. **Verification Method:** Review rules of behavior, confirm consent flows, and audit public project listings for adherence. **Priority:** Medium ##### ISO 27001:2022 Controls **A.18.1.4** **Requirement:** When making information public, procedures must ensure compliance with applicable privacy regulations and consent requirements. **Relevance:** Ensures that public sharing respects privacy laws and consent obligations, critical for public showcases to avoid PII exposure. **Integration Tips:** Include PII detection in publish flow, require data owner sign-off for content containing possible PII, and record consent evidence. **Verification Method:** Review publish-time checks for PII, consent capture records, and legal/compliance reviews of public content. **Priority:** Medium #### 6.2.21. REQ-021: Integration adapters for cloud storage (S3/GCS/Azure), external ML platforms and CI/CD ##### OWASP ASVS Controls **ASVS 12.1** **Requirement:** Integrations to cloud storage and external services must use strong authentication, least privilege, and secure credential handling (e.g., short-lived tokens). **Relevance:** Directly pertains to adapters connecting to S3/GCS/Azure and other platforms: requires secure auth and credential management. **Integration Tips:** Use short-lived credentials (STS), role-based access, and avoid long-lived secrets; rotate credentials and store them in a secrets manager. **Verification Method:** Inspect credential handling, verify use of short-lived tokens, and review permissions granted to adapter service accounts. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-17** **Requirement:** Remote Access: Control and secure remote connections to external services and cloud resources; enforce strong authentication and encryption. **Relevance:** Applies to remote connections to cloud services used by adapters and demands secure channels and authentication. **Integration Tips:** Enforce TLS for all remote connections, mandate mutual TLS where appropriate, and log remote access for audit. **Verification Method:** Review network/TLS configurations and logs of remote connections to cloud services. **Priority:** High ##### ISO 27001:2022 Controls **A.14.2.7** **Requirement:** Where functions are outsourced or integrated externally, security requirements must be defined and enforced, including credential and access controls. **Relevance:** If adapters integrate with external services or third-party platforms, security requirements for those relationships must be established. **Integration Tips:** Define SLAs/security requirements for integrations and ensure secure onboarding of third-party connectors with scoped access and audits. **Verification Method:** Review third-party agreements, connector security assessments, and integration onboarding documentation. **Priority:** Medium #### 6.2.22. REQ-022: Secure APIs for artifact storage, model deployment, and telemetry ingestion ##### OWASP ASVS Controls **ASVS 14.3** **Requirement:** APIs must authenticate and authorize requests, validate input, enforce rate-limiting, use TLS, and protect against common API attacks. **Relevance:** Directly covers security expectations for APIs handling artifacts, deployments, and telemetry ingestion to prevent abuse and data compromise. **Integration Tips:** Enforce token-based auth (OAuth2/JWT) with scopes, validate all inputs and payloads, apply rate limits, and require TLS with strong ciphers. **Verification Method:** API security testing (fuzzing, auth bypass tests), review TLS configs, and examine rate-limit enforcement logs. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **SC-13** **Requirement:** Cryptographic Protection: Protect API communications and sensitive data in transit with approved cryptography. **Relevance:** Mandates cryptographic protection of API communications, essential for telemetry and artifact transfers containing sensitive info. **Integration Tips:** Use TLS for transport, encrypt payloads where necessary, and manage keys with a KMS. Avoid weak cipher suites and protocol versions. **Verification Method:** Review TLS termination points, cipher suite lists, and key management practices. **Priority:** High ##### ISO 27001:2022 Controls **A.9.2.6** **Requirement:** Use of privileged interfaces and APIs must be restricted and controlled with appropriate authentication and authorization. **Relevance:** APIs for deployment/ingestion may provide privileged operations; this control ensures their usage is restricted and audited. **Integration Tips:** Segment privileged API endpoints, require higher assurance (MFA, IP allowlists) for privileged ops, and log/monitor privileged API calls. **Verification Method:** Review access controls for privileged endpoints, inspect logs of privileged calls, and test enforcement of additional protections. **Priority:** High #### 6.2.23. REQ-023: Data retention, export, deletion and data subject rights workflows for compliance ##### OWASP ASVS Controls **ASVS 10.6.2** **Requirement:** Data retention and deletion policies must be enforced programmatically; provide mechanisms to export and delete personal data in accordance with legal requirements. **Relevance:** Directly applicable to retention/export/deletion and data subject workflows supporting regulatory compliance (e.g., GDPR). **Integration Tips:** Implement automated retention enforcement, authenticated export endpoints, and deletion flows that remove data from live and archived stores as required. **Verification Method:** Test retention enforcement, perform data export requests, and validate deletion propagation and logs. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **MP-6** **Requirement:** Media Sanitization: Ensure secure disposal and sanitization of media and data when no longer required. **Relevance:** Covers secure sanitization for deleted data and ensures exports/deletions actually remove data from all media including backups. **Integration Tips:** Document sanitization procedures for primary and backup storage, use cryptographic erasure where possible, and validate deletion completion. **Verification Method:** Perform recovery attempts on sanitized media, and review sanitization logs and procedures. **Priority:** High ##### ISO 27001:2022 Controls **A.18.1.4** **Requirement:** Organizations must adhere to legal and regulatory requirements relating to data retention and subject rights. **Relevance:** Mandates alignment with legal obligations for retention and data subject rights, applying to export and deletion workflows. **Integration Tips:** Maintain a legally-aligned retention schedule, provide clear DSAR (data subject access request) processes, and log all DSAR activities. **Verification Method:** Review retention schedules, test DSAR handling, and audit logs for DSAR fulfillment. **Priority:** Critical #### 6.2.24. REQ-024: Audit, logging, encryption, and monitoring for security and operational observability ##### OWASP ASVS Controls **ASVS 11.2** **Requirement:** Implement sufficient logging, secure log storage, and monitoring to detect security incidents; ensure logs are integrity-protected and access-controlled. **Relevance:** Directly addresses core requirements for logging, encryption of logs, and monitoring to support security and operations observability. **Integration Tips:** Protect logs with write-once or HMAC integrity, centralize logs in SIEM, restrict log access, and encrypt logs at rest and in transit. **Verification Method:** Inspect log integrity protections, review SIEM alerts, and test log access controls. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **IA-5(1)** **Requirement:** Use of cryptographic authentication mechanisms and key management controls to protect secrets and authentication tokens. **Relevance:** Key management and cryptography underpin secure storage of secrets, encryption of telemetry, and protection of logs and artifacts. **Integration Tips:** Use a centralized KMS for key lifecycle, protect authentication tokens using hardware-backed storage where possible, and rotate keys regularly. **Verification Method:** Review key management practices, KMS configuration, and evidence of key rotation and access controls. **Priority:** High ##### ISO 27001:2022 Controls **A.10.1.1** **Requirement:** Use cryptographic controls to protect confidentiality, integrity of information and manage keys appropriately. **Relevance:** Provides policy-level mandate to use cryptography for encrypting sensitive logs, telemetry, artifacts and for overall observability data protection. **Integration Tips:** Create cryptography policy, select approved algorithms, and integrate cryptographic controls into logging and telemetry pipelines with KMS support. **Verification Method:** Review cryptography policy and check that deployed systems use approved algorithms and key management according to policy. **Priority:** High #### 6.2.25. REQ-025: Administrative features: tenant settings, billing integration, usage reporting, and quota management ##### OWASP ASVS Controls **ASVS 5.5** **Requirement:** Administrative interfaces must be protected with high assurance authentication, authorization, and auditability; separate admin functions from normal user functions. **Relevance:** Administrative tenant/billing interfaces require high protection to avoid misuse or fraudulent changes; separation from user functions is crucial. **Integration Tips:** Enforce MFA for admin accounts, restrict admin UI by IP or role, segregate admin APIs, and maintain detailed audit logs for all admin actions. **Verification Method:** Test admin authentication flows (MFA), review segregation of admin endpoints, and inspect admin action logs. **Level:** L2 | **Priority:** Critical ##### NIST SP 800-53 Controls **AC-5** **Requirement:** Separation of Duties: The system must support separation of duties and prevent conflict of interest by restricting access to administrative functions. **Relevance:** Prevents a single administrative user from performing incompatible tasks on billing/tenant settings, mitigating risk of fraud or abuse. **Integration Tips:** Implement RBAC that separates billing, tenant admin, and audit roles; require dual-control for sensitive billing changes. **Verification Method:** Review role separation, test that a single role cannot perform conflicting admin actions, and inspect change logs. **Priority:** High ##### ISO 27001:2022 Controls **A.18.2.3** **Requirement:** Systems performing administrative or billing tasks should be subject to technical reviews to ensure compliance with security requirements. **Relevance:** Mandates technical reviews/audits of admin/billing modules to ensure they meet security and compliance expectations. **Integration Tips:** Schedule regular security reviews for admin components, include billing integrations in compliance scans, and remediate findings promptly. **Verification Method:** Review technical audit reports and confirm remediation of identified issues. **Priority:** Medium ### 6.3. Cross-Functional Security Controls The following controls apply globally across all system components: **Logging and Audit** *Description:* Comprehensive logging of security-relevant events, user actions, admin actions, and system changes with tamper-evidence and retention policies. *Applies to:* All requirements that involve user actions, admin operations, data changes, sharing, deployments, AI outputs, and integrations *Implementation Guidance:* Centralize logs into a SIEM with access controls, ensure logs are integrity-protected (WORM/HMAC), include contextual metadata (user, project, request-id), and define retention/archival policies aligned with compliance. **Encryption and Key Management** *Description:* Use approved cryptography to protect data at rest and in transit and manage keys via a centralized KMS with lifecycle controls. *Applies to:* Authentication tokens, chat histories, artifacts, model binaries, backups, telemetry, and API communications *Implementation Guidance:* Encrypt data at rest with AES-256 or approved alternatives, enforce TLS for transport, use KMS for key rotation and access controls, and implement cryptographic policies and audits. **Access Control and RBAC/ABAC** *Description:* Centralized authorization system enforcing least privilege, separation of duties, and object-level policies (roles, tags, metadata). *Applies to:* User management, projects, datasets, model registry, sharing features, and admin interfaces *Implementation Guidance:* Implement server-side enforcement, use ABAC for tag/metadata-based rules, integrate with identity providers for SSO, and perform periodic access reviews and audits. **Input/Output Sanitization and Safe Rendering** *Description:* Sanitize all inbound content (uploads, comments, templates) and apply context-sensitive encoding for outputs to prevent XSS and injection. *Applies to:* File uploads, dashboards, comments, chat, visualizations, exports *Implementation Guidance:* Validate and sanitize inputs, use safe rendering libraries for HTML/SVG, restrict allowed markup in user content, and strip metadata from exported artifacts. **Data Subject Rights and Retention Management** *Description:* Programmatic enforcement of retention policies, and processes supporting export and deletion requests (DSARs) with verification and propagation to backups. *Applies to:* Chat history, datasets, user profiles, telemetry, and backups *Implementation Guidance:* Provide authenticated DSAR interfaces, maintain audit trails for requests, implement deletion workflows that handle backups, and ensure compliance with legal retention requirements. ### 6.4. Requirements Traceability Overview This section demonstrates complete traceability from high-level requirements through threats to security controls and verification methods. **Coverage Summary:** Traceability matrix contains 35 requirements. 35 requirements (100.0%) linked to threats. 25 requirements (71.4%) mapped to security controls (OWASP ASVS, NIST SP 800-53, ISO 27001). Coverage: Partial. #### Sample Traceability Mappings The following table shows traceability for high-priority requirements: | Req ID | Requirement | Threats | Security Controls | Standards | Priority | Verification | |--------|-------------|---------|-------------------|-----------|----------|-------------| | REQ-001 | User registration and login supporting e... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews. | | REQ-002 | Enterprise Single Sign-On (SSO) via OIDC... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected. | | REQ-003 | Role-based access control (RBAC) support... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions. | | REQ-005 | User profile management including avatar... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes. | | REQ-006 | Project creation and organization with d... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Test access to objects with different tag/ownership combinations and review policy evaluation logs. | | REQ-007 | Experiment tracking with artifact versio... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity. | | REQ-008 | Side-by-side experiment comparison UI wi... | 9 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity. | | REQ-010 | Activity feed and audit logging for proj... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Check log retention configurations, review access controls to logs, and confirm logs include required event types. | | REQ-011 | Conversational AI assistant accessible v... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts. | | REQ-012 | Natural language search across experimen... | 10 threats | 3 controls | ISO27001, NIST, OWASP | Critical | Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls. | *Showing 10 of 35 requirements. See Appendix D for complete traceability matrix.* #### Traceability Statistics - **Total Requirements Tracked:** 35 - **Requirements Linked to Threats:** 35 (100.0%) - **Requirements Mapped to Controls:** 25 (71.4%) - **Average Controls per Requirement:** 2.1 - **Control Distribution by Standard:** - OWASP ASVS: 25 controls - NIST SP 800-53: 25 controls - ISO 27001: 25 controls - **Verification Coverage:** 100% (all requirements have verification methods) --- ## 7. AI/ML Security Requirements This section addresses security requirements specific to artificial intelligence and machine learning components within the system. AI/ML systems introduce unique security challenges including prompt injection attacks, data poisoning, model theft, adversarial inputs, and bias vulnerabilities. This analysis identifies AI/ML components, assesses their security risks, and prescribes specialized controls to protect both the AI systems themselves and the data they process. ### 7.1. AI/ML Components Detected This section identifies all AI/ML components within the system that require specialized security controls. 1. **Conversational AI Assistant (LLM)**: Provides natural language queries to search experiments, code suggestions, and context-aware help using large language models. 2. **Experiment Tracking and Insights**: Utilizes AI to automate insights and generate summaries from experiment results. 3. **Model Monitoring Dashboard**: Employs machine learning models to monitor prediction accuracy, drift, and provide A/B testing capabilities. ### 7.2. AI/ML Threat Model | Component | Identified Threats | |---------------------------------------|---------------------------------------------------------| | Conversational AI Assistant (LLM) | - Prompt injection | | | - Adversarial input attacks | | | - Data leakage through training data | | Experiment Tracking and Insights | - Data leakage (PII in training prompts) | | | - Model inversion attacks | | Model Monitoring Dashboard | - Model poisoning | | | - Supply chain vulnerabilities | ### 7.3. AI/ML Security Controls #### Conversational AI Assistant (LLM) - **Prompt Injection Prevention**: Implement strict validation of user inputs to prevent malicious prompts. Use a whitelist approach to allow only safe commands. - **Output Filtering and Sanitization**: Sanitize outputs generated by the AI to remove sensitive information and ensure compliance with data protection regulations. - **Rate Limiting and Abuse Prevention**: Monitor and limit the frequency of requests to the AI assistant to mitigate abuse and ensure fair usage among users. #### Experiment Tracking and Insights - **Input Validation for AI Inputs**: Validate inputs to the AI system to prevent injection attacks and ensure that only expected data formats are processed. - **Model Access Controls**: Develop strict access controls around who can view or modify experiment data and insights to protect proprietary information. - **Monitoring for Adversarial Inputs**: Implement monitoring mechanisms to detect unusual patterns or inputs that could indicate adversarial attacks on the model. #### Model Monitoring Dashboard - **Model Versioning and Rollback Capabilities**: Ensure that all models are versioned, allowing rollback to a previous stable state in case of detected anomalies or poisoning. - **Supply Chain Security for Models**: Implement checks to verify the integrity of models and datasets sourced from external providers to prevent supply chain attacks. - **Bias and Fairness Considerations**: Regularly audit models for bias and implement fairness checks to ensure equitable outcomes across diverse user groups. ### 7.4. Integration with Existing Security Controls AI controls integrate with standard security practices by complementing user authentication methods (like 2FA) to secure access to sensitive AI components. Additionally, regular audits and logging mechanisms established for general application security will also encompass AI-specific logs for monitoring model performance and detecting anomalies. Data retention and compliance workflows for PII will be extended to cover AI-generated data and interactions, ensuring comprehensive protection. ### 7.5. AI/ML Monitoring Requirements | Monitoring Area | Description | |-------------------------------------|-----------------------------------------------------| | User Interaction Monitoring | Track user interactions with the AI assistant to detect unusual patterns or potential abuse. | | Model Performance Monitoring | Continuously monitor model accuracy, drift, and performance metrics to ensure reliability and compliance. | | Audit Logging | Maintain detailed logs of all AI-related operations and user interactions for accountability and forensic analysis. | | Data Access Auditing | Regularly audit accesses to datasets and results generated by the AI to protect sensitive information. | --- ## 8. Compliance Requirements This section identifies regulatory and legal compliance obligations applicable to the system based on data types, geographic scope, industry sector, and business operations. Compliance requirements drive specific security controls, data handling procedures, audit capabilities, and privacy protections. Non-compliance can result in significant legal penalties, reputational damage, and business disruption. This analysis maps applicable regulations to specific security requirements and operational procedures. ### 8.1. Applicable Regulations Regulations were identified based on the types of data the AI-Powered ML Workflow Management Web Application will handle, including user information, project data, and potential integration with external systems. The geographic scope includes regions such as the EU and California, which necessitate compliance with specific data protection laws. The industry sector primarily relates to technology and data processing, which is subject to stringent privacy regulations. Business operations include machine learning and data management, further highlighting applicable compliance requirements. Compliance requirements directly impact security controls, data handling procedures, and operational processes. | Regulation | Applicability Reason | |------------|----------------------| | GDPR | Applies because the system processes personal data of EU residents, including user registration and profile management. | | CCPA | Applies due to the handling of personal data of California residents, requiring transparency and consumer rights. | | HIPAA | May apply if health-related data is processed or if the application is used in healthcare settings. | | PCI-DSS | Relevant if the application processes payment card data, which could occur during billing integrations. | | SOX | Applies if the application handles financial data relevant to public companies, impacting audit and reporting functions. | | COPPA | Relevant if any data concerning children under 13 years is collected, necessitating parental consent. | | Data Residency Laws | Important for compliance when storing data in specific geographic locations, impacting cloud storage integrations. | | GLBA | May apply if the application deals with nonpublic personal information in financial contexts. | ### 8.2. Compliance Controls by Regulation #### GDPR - User consent management for personal data processing. - Data minimization and purpose limitation controls. - Security measures including encryption and access controls. - Data subject access request (DSAR) mechanisms. #### CCPA - Transparent data collection notices. - Consumer opt-out mechanisms for data selling. - Enhanced rights for consumers to access and delete their data. - Annual reporting on data handling practices. #### HIPAA - Secure storage and transmission of health information. - Access controls and audit logs for health data. - Business Associate Agreements with third-party service providers. #### PCI-DSS - Secure handling of payment card data. - Implementation of strong access control measures. - Regular security testing and vulnerability management. #### SOX - Financial data handling procedures for accuracy and security. - Regular audits and internal controls documentation. - Retention of financial records and communications. #### COPPA - Parental consent verification mechanisms. - Age verification processes for child user accounts. - Clear privacy policies regarding children's data. #### Data Residency Laws - Data localization practices based on jurisdictional requirements. - Compliance with international data transfer regulations. #### GLBA - Protection of nonpublic personal information. - Privacy notices and consumer opt-out rights. ### 8.3. Data Subject Rights | Right | Description | |-----------------------|--------------------------------------------------------------| | Right to Access | Users can request access to their personal data. | | Right to Rectification| Users can request correction of inaccurate personal data. | | Right to Erasure | Users can request deletion of their personal data. | | Right to Data Portability | Users can request a copy of their personal data in a portable format. | | Right to Object | Users can object to the processing of their personal data. | | Right to Withdraw Consent | Users can withdraw consent for data processing at any time. | ### 8.4. Privacy Requirements **Consent:** Users must provide explicit consent for data collection and processing. **Privacy Notices:** Clear and accessible privacy notices must be provided to users detailing data handling practices. **Data Protection Impact Assessment (DPIA):** Conduct DPIAs to assess risks associated with data processing activities. ### 8.5. Audit and Monitoring Requirements **Logging:** Maintain detailed logs of data access and modifications to ensure traceability. **Monitoring:** Continuous monitoring of systems for security breaches and compliance violations. **Audit Trails:** Maintain audit trails for all data processing activities to support compliance audits. ### 8.6. Data Handling Rules **Retention:** Personal data must be retained only as long as necessary for its intended purpose. **Deletion:** Clear procedures for the secure deletion of personal data upon request or when no longer needed. **Data Minimization:** Collect only the data necessary for the application's functionality and purposes. ### 8.7. Compliance Risk Assessment Compliance risks associated with the application include potential breaches of user data, inadequate consent mechanisms, and non-compliance with financial reporting requirements. Addressing these risks is crucial for maintaining user trust and meeting legal obligations. **Key Compliance Risks:** - Risk of data breaches leading to unauthorized access to personal data. - Risk of inadequate user consent management resulting in non-compliance. - Risk of failing to meet financial data handling requirements under SOX and GLBA. --- ## 9. Security Architecture Recommendations This section provides comprehensive security architecture guidance that integrates security controls into the system's technical design. Security architecture defines how security principles, controls, and patterns are applied across system components to create a cohesive, defense-in-depth security posture. The recommendations address architectural principles, component-level controls, data protection strategies, and third-party integration security to ensure security is built into the system design. ### 9.1. Architectural Security Principles Architectural security principles provide the foundational philosophy guiding all security design decisions. These principles ensure a consistent security posture across system components, drive selection and implementation of controls, and enable defensible, auditable security outcomes that scale with multi-tenant, cloud-hosted ML workflows. - **Zero Trust Architecture principles**: Never trust, always verify — every access request (user, service, or device) must be authenticated, authorized, and continuously validated regardless of network location. This prevents implicit trust of network segments or systems and mitigates insider and lateral movement risks. - **Defense in Depth**: Layer multiple complementary controls (network, identity, platform, application, data) so that a failure in one layer does not result in system compromise. Redundancy and diverse controls increase attacker effort and reduce blast radius. - **Principle of Least Privilege**: Grant users, services, and workloads only the minimum privileges required for tasks; enforce just-in-time and scoped access with time-limited credentials. Minimizes impact of credential compromise. - **Secure by Default / Secure by Design**: Default configurations should be the most secure: hardening, disabled debug, least-open network rules, secure headers, and safe defaults for sharing/publishing. Security is embedded in design and CI/CD pipelines rather than bolted on post-deployment. - **Separation of Duties**: Partition roles and capabilities to prevent a single principal from performing conflicting privileged operations (e.g., deploy + approve billing). Use RBAC and approval workflows for critical actions. - **Fail Secure (Safe Failure)**: Systems should fail into a secure state (deny-by-default) on errors or degraded operations, avoiding silent fallback to insecure behavior (e.g., disabling authentication). - **Complete Mediation**: Every access to a resource must be checked against the current policy or permission set (no cached, bypassable checks). Centralize authorization and ensure enforcement at service/resource boundaries. - **Auditability and Non-Repudiation**: Design for comprehensive, tamper-evident logging and provenance tracking across experiments, artifacts, deployments and AI interactions to support investigations, compliance, and model governance. - **Privacy-by-Design and Data Minimization**: Collect and surface only the minimum data required for features; redact or anonymize sensitive items before sending to external LLMs or indexing. - **Resilience and Observability**: Include robust telemetry, health checks, and incident runbooks; monitor security and performance signals to detect anomalies early and automate mitigations where appropriate. --- ### 9.2. Component-Level Security Controls #### Frontend User Interface **Required Controls:** - Strong authentication flows integrated with Identity Providers (OIDC/SAML/social OAuth) and session management with secure cookies (HttpOnly, Secure, SameSite). - Client-side input validation and consistent server-side validation of all inputs. - Content sanitization and context-sensitive encoding for all rendered content (HTML, SVG, JS). - CSP, secure headers (HSTS, X-Frame-Options, X-Content-Type-Options, Referrer-Policy), and SRI for third-party scripts. - Secure file upload initiation with direct-to-object-storage upload patterns and signed, ephemeral upload URLs. - Local caching controls and safe offline UX that protects tokens and sensitive caches. - Strict CORS configuration scoped to trusted origins and subdomains. - Privacy controls/UI for opt-in public sharing and consent capture for chat/export features. **Recommended Patterns:** - Serve SPA through CDN + WAF with edge authentication and bot protections. - Use token-based auth (short-lived JWTs + refresh token rotation) and store only refresh tokens in secure, same-site cookies. - Implement Content Security Policy and Subresource Integrity for external assets. - Direct-to-cloud-storage uploads (presigned URLs, multipart) with upload validation and server-side post-commit verification. #### Edge / API Gateway **Required Controls:** - TLS termination with strong ciphers and TLS 1.2/1.3 enforcement. - Authentication proxy validating tokens (OIDC/OAuth2 introspection/JWKS verification), SSO assertions (SAML) and enforcing scopes. - WAF rulesets, OWASP CRS, and adaptive rate limiting per tenant and per API. - Request validation and schema enforcement (reject malformed/payloads exceeding expected sizes). - Centralized request logging and request-id propagation for traceability. - IP allowlisting for privileged endpoints and geo/IP threat blocking where appropriate. **Recommended Patterns:** - API Gateway with OAuth2 token validation and RBAC scope enforcement. - Centralized edge WAF with custom rules for ML artifact and binary handling. - Edge caching with cache key segregation per tenant and cache purge on unpublish. - Mutual TLS (mTLS) for connections to backend components requiring high assurance. #### Application Services **Required Controls:** - Centralized authorization (RBAC + ABAC) enforcement for all resource access, with server-side policy evaluation. - Input validation, strong parameterized DB queries / ORM safe practices to prevent injection. - Service-to-service authentication using short-lived mTLS tokens or signed JWTs with audience claims. - Immutability and signature checks for critical artifacts and templates. - Per-tenant isolation (logical DB schemas or row-level tenancy) and resource quotas. - Instrumentation for auditing actions and generating activity feed events. **Recommended Patterns:** - Microservices with API Gateway and service mesh for observable service-to-service auth and encryption. - Centralized policy engine (e.g., OPA) for ABAC and dynamic policy decisions. - Use of application-level request throttling and per-tenant quota enforcement. #### AI Assistant & LLM Orchestration **Required Controls:** - Context sanitization and PII detection before sending any context to external LLMs; configurable per-tenant redaction policies. - Policy enforcement for model selection and usage (tenant-level allow/block lists for external providers). - Logging of prompts and model outputs with access controls and retention rules (with redaction where required). - Output filtering to detect and redact secrets, PII, and sensitive code snippets. - Per-tenant privacy options: opt-out of external LLMs, use private/enterprise LLMs or on-premise LLMs. - Rate limiting and quota enforcement on LLM usage to control cost and abuse. **Recommended Patterns:** - LLM orchestration layer that mediates providers, caches safe responses, and routes requests based on tenant policy. - Use vector DB with encrypted-at-rest storage for embeddings and per-tenant namespaces/indices. - Human-in-the-loop approval workflows for high-risk LLM outputs. - Differential privacy techniques or privacy-preserving embeddings when processing sensitive datasets. #### Realtime Collaboration & Notifications **Required Controls:** - Encrypted transport (TLS + WebSocket over TLS) and authentication for socket connections with token rotation. - Authorization checks on each real-time message and presence event to ensure users only see permitted state. - Rate limiting and abuse detection for messaging and @mention features. - Sanitization and safe rendering of user-generated content to prevent XSS and injections in live updates. - Secure storage for persisted chat/comment threads with access controls and retention rules. **Recommended Patterns:** - Use managed realtime services with integration to identity tokens OR self-hosted signaling layer behind service mesh. - Channel or room-level ACLs and capacity-based throttling. - Message signing and sequence validation to detect tampering or replay. #### Data Storage **Required Controls:** - Encryption at rest for all persistent stores using KMS-managed keys and envelope encryption. - Fine-grained access controls and tenant isolation (database row-level security or separate instances for high-risk tenants). - Immutable append-only audit logs with tamper-evidence (WORM/HMAC chaining). - Object storage policies for artifact lifecycle (versioning, lifecycle rules, access logs). - Data integrity checks (checksums, content-addressable storage) and artifact signing. **Recommended Patterns:** - Use managed relational DB with TDE and row-level security for metadata. - Object storage (S3/GCS) with bucket-level policies, encryption, versioning, and signed URLs for access. - Vector DB per-tenant or namespaced indices with RBAC at query time. - Time-series DB for metrics with retention tiers and downsampling pipelines. #### Integration & Deployment Services **Required Controls:** - Short-lived credentials (STS, OIDC tokens) for cloud integrations and least-privilege service roles for external ML infra. - Signed model artifacts and provenance metadata enforced prior to deployment. - Secure CI/CD pipelines with secure secret storage and git commit signing. - Deployment approvals (change management integration) and separation between staging/production credentials. - Audit trails for deployment actions and rollback capabilities. **Recommended Patterns:** - GitOps-style deployment with artifact signing (cosign/sigstore) and automated policy gates. - Use of Secrets Manager & KMS for pipeline secrets and ephemeral runner credentials. - Canary and blue/green deployment patterns with feature flags and automated rollback triggers. #### Platform & Ops **Required Controls:** - Centralized KMS/HSM for cryptographic keys and BYOK support for enterprise tenants. - SIEM integration with alerting and playbooks; EDR for host detection and response. - Hardened cluster configuration (CIS benchmarks) and RBAC/PodSecurityPolicies for runtime. - Backup encryption and documented sanitization/restore procedures. - Role-separation for operational access with just-in-time elevated access and session recording. **Recommended Patterns:** - Manage platform via IaC with security reviews in PR pipelines and automated policy-as-code checks. - Use of secrets scanning, SCA, SAST, DAST integrated into CI/CD. - Multi-account/multi-project separation per environment and tenant isolation. #### External Services **Required Controls:** - Contractual and technical controls: vetted CSP/LLM/IDP providers, defined SLAs and security terms. - Secure credential handling and least-privilege access for connectors. - Monitoring and anomaly detection for external API usage and outbound data flows. - Explicit data residency and privacy controls where required by tenant/regulator. **Recommended Patterns:** - Use gateway/proxy for external API calls to centralize controls (mTLS, token refresh, audit). - Provider isolation (different service accounts per provider) and per-tenant provider configuration. --- ### 9.3. Data Protection Strategy **Data Classification:** Public — Non-sensitive content safe for public exposure (public model cards, public showcase artifacts). Internal — Non-sensitive operational data intended only for authenticated users within the organization (UI preferences, non-sensitive project metadata). Confidential — Sensitive project data, datasets, experiment metadata that include PII, proprietary code or IP, model artifacts that are not public. Restricted — Highly sensitive data (regulated health data, financial data, secrets, private customer data) requiring strongest controls and contractual protections (HIPAA, PCI, or other regulatory regimes). **Encryption Requirements:** - Data in transit: TLS 1.2 minimum; TLS 1.3 preferred. Enforce strong cipher suites (AEAD: AES-GCM, ChaCha20-Poly1305). - Data at rest: AES-256-GCM (or equivalent approved AEAD) for object and block storage. Envelope encryption model: data keys encrypted by KMS-managed master keys. - Key management: Centralized KMS (cloud KMS or HSM) with BYOK/HSM option for enterprise tenants. Use RSA-3072 or RSA-4096 for legacy signing where needed; prefer ECC (P-256/P-384) for signing and ECDH key exchange where supported. - Token/secret protection: Store secrets in a secrets manager; do not persist plain tokens. Use hardware-backed storage for highly sensitive keys. - Integrity: Use SHA-256 or stronger for checksums; sign artifacts with sigstore / cosign to verify provenance. **Retention Policies:** - Audit logs (tamper-evident): configurable, default 7 years (or longer per regulatory requirement). - Experiment metadata and model registry entries: default 3 years; configurable per-tenant; mark as archived after inactivity. - Chat transcripts: default 1 year; configurable per-tenant and subject to DSARs; option to auto-redact sensitive content or disable storage. - Telemetry/metrics: raw detailed telemetry retained 90 days; aggregated metrics retained 2 years. - Backups: retention per backup class and compliance obligations (e.g., 90 days for daily snapshots, long-term snapshots per legal requirements), with secure retention and periodic sanitization procedures. - DSR/Deletion retention: deletion requests must propagate to primary and backup stores within defined SLA (e.g., 30 days), documented by the data subject request workflow. **Handling Procedures:** - Access: Enforce RBAC/ABAC with central policy engine and just-in-time escalation for sensitive operations. All accesses must be logged with request-id, user, tenant, resource, action, and reason. - Transmission: All external interactions use TLS; high-sensitivity flows require mTLS or VPN; use out-bound proxies to centralize egress controls. - Storage: Use per-tenant namespaces/ACLs in object and DB storage; enable versioning and retention lifecycle policies; scans to detect PII/secret artifacts upon ingest. - Deletion: Implement soft-delete then secure erasure/cryptographic key deletion for data purging; include backup sanitization and documented proof of deletion for DSARs. - Minimization & redaction: Before sending any context to external LLMs, run PII detection and redaction. Provide tenant controls to prevent external transmission. - Data provenance & lineage: Record immutably who/what/when produced datasets/models and linking training data, hyperparameters, code and environment. - Export/DR: Exports must be authenticated, rate-limited, and delivered over secure channels; exported packages should be encrypted in transit and at rest. --- ### 9.4. Third-Party Integration Security **Identity Providers (OIDC/SAML/Social OAuth)** *Security Requirements:* - Use provider token validation via JWKS and validate audience/issuer/nonce. - Support SAML assertion verification and replay protection (timestamps/nonces). - Use short-lived tokens and implement proactive revocation flows. - Capture and store consent evidence and provisioning metadata. *Risk Assessment:* High — Identity compromise or misconfiguration can lead to large-scale unauthorized access; SSO misvalidation results in impersonation. *Recommended Controls:* - Enforce strict claim verification, nonce checks, and replay detection. - Integrate with centralized IAM for account provisioning/deprovisioning and access reviews. - Log SSO events and monitor anomalous login patterns. - Offer federation security (SCIM for provisioning) with encrypted metadata endpoints. **LLM Providers (OpenAI / Anthropic / Enterprise / Private On-Prem LLMs)** *Security Requirements:* - Use authenticated APIs (API keys, OAuth2, or mTLS). - Maintain per-tenant provider configuration and denylist/allowlist options. - Protect prompts and outputs with logging and access controls; enable tenant opt-out of external providers. *Risk Assessment:* High — data leakage, prompt/output exfiltration of secrets or PII, regulatory exposure when sending sensitive data externally. *Recommended Controls:* - Implement in-orchestration PII detection and redaction before external calls. - Use provider-specific enterprise agreements that prohibit model training on customer data (where needed). - Enforce per-tenant routing policies and rate limits. - Retain prompt/output logs in encrypted stores and restrict access. **Cloud Storage (S3 / GCS / Azure Blob)** *Security Requirements:* - Use scoped IAM roles and short-lived STS credentials for access. - Encrypt objects at rest with customer-managed keys. - Enforce bucket/network policies and VPC Service Controls where available. *Risk Assessment:* High — misconfigured buckets or leaked credentials can expose datasets and model artifacts. *Recommended Controls:* - Use least-privilege service accounts and distinct roles per integration. - Enable object-level ACL auditing and access logs (S3 access logging / GCS audit logs). - Apply lifecycle rules, versioning and object lock (WORM) for audit artifacts. **External ML Platforms (SageMaker, Kubeflow, Vertex AI, etc.)** *Security Requirements:* - Authentication via short-lived service account tokens or OIDC. - Scoped permissions for dataset/model access and runtime isolation. - Secure artifact transfer (signed artifacts, TLS). *Risk Assessment:* Medium-High — cross-platform integrations can leak data or misconfigure runtime permissions; model execution environments could be attacked. *Recommended Controls:* - Use per-tenant service accounts with minimal permissions. - Enforce artifact signing and verification before deployment. - Monitor external job creation and runtime behavior; integrate logs into SIEM. **CI/CD Systems and Build Runners** *Security Requirements:* - Secrets must be retrieved from secure secrets manager; build artifacts signed at build time. - Runner isolation and ephemeral credentials; RBAC for job triggers. *Risk Assessment:* High — CI compromise can inject malicious model code or exfiltrate artifacts. *Recommended Controls:* - Use ephemeral runner tokens and OIDC-issued credentials. - Enforce SCA, SAST and artifact signing in pipelines. - Audit CI activity and require code reviews for deployment-critical changes. **CDN / WAF Providers** *Security Requirements:* - TLS termination with strong ciphers; WAF policies for OWASP CRS. - Edge rules for bot mitigation and DDoS protections. *Risk Assessment:* Medium — misconfig or WAF rules can allow application-layer attacks or block legitimate traffic. *Recommended Controls:* - Central WAF rule management with staging/test rulesets. - Monitor and tune rules; maintain allowlists for admin access. - Use origin authentication (signed headers/mTLS) to prevent origin spoofing. **Vector DB / Search Provider** *Security Requirements:* - Enforce per-tenant namespaces and access controls on queries. - Encrypt embeddings at rest; protect vector store admin APIs. *Risk Assessment:* Medium-High — embeddings may leak sensitive semantic content; search can be used for privacy enumeration. *Recommended Controls:* - RBAC and query-level filtering to enforce row-level security. - Rate limiting and logging for search queries to detect enumeration. - Redaction and transformation of sensitive fields before indexing. **Billing / Payment Gateway** *Security Requirements:* - PCI-compliant handling if payment data is stored or processed. - Use tokenization and never store raw card data. *Risk Assessment:* Medium — financial data exposure leads to regulatory and reputational risk. *Recommended Controls:* - Integrate with PCI-compliant gateways using tokenization. - Restrict billing admin operations with dual-control approvals. - Monitor billing anomalies and reconcile billing logs. **Monitoring / SIEM / Alerting Vendors** *Security Requirements:* - Secure log ingestion (TLS), authenticated ingest keys, and retention controls. - RBAC for access to SIEM dashboards and threat data. *Risk Assessment:* Medium — exposing monitoring data may reveal system internals and user activity. *Recommended Controls:* - Encrypt logs in transit and at rest; integrate with KMS. - Limit access to SIEM with strong authentication and session recording. - Alert escalation with defined playbooks and automated containment where appropriate. **Enterprise Identity / HR Systems (SCIM, Provisioning)** *Security Requirements:* - Use SCIM over TLS, with scoped credentials and audit of provisioning actions. - Synchronize deprovisioning events and maintain authoritative sources of truth. *Risk Assessment:* Medium — mis-synced accounts cause orphaned access or unnecessary access persistence. *Recommended Controls:* - Implement automated deprovisioning and regular reconciliation. - Log provisioning events and require MFA for provisioning UI. --- (End of document) --- ## 10. Implementation Roadmap This section provides a prioritized, phased approach for implementing the security controls identified throughout this analysis. The roadmap organizes security measures into logical phases based on risk, dependencies, and resource availability, ensuring critical security gaps are addressed first while building a foundation for comprehensive security coverage. ### 10.1. Prioritization Framework Prioritization is critical for effective security implementation as it ensures that the most critical risks are addressed first, compliance deadlines are met, and resources are efficiently allocated. By prioritizing security controls, the organization can strategically mitigate risks, adhere to regulatory requirements, and align security initiatives with business objectives. **Prioritization Criteria:** - **Risk Level**: Controls addressing critical and high-risk threats (identified through threat modeling) are prioritized first. - **Compliance Deadlines**: Regulatory requirements and compliance deadlines influence immediate priority. - **Technical Complexity**: Controls requiring foundational infrastructure are implemented early to enable subsequent controls. - **Dependencies**: Controls that other security measures depend upon are prioritized accordingly. - **Resource Availability**: Implementation considers the availability of skilled personnel, tools, and budget. - **Business Impact**: Controls protecting business-critical functions and data receive higher priority. These criteria work together to create a logical implementation sequence that balances security needs with practical constraints, ensuring that the most pressing risks are mitigated first while building a robust security foundation for future enhancements. ### 10.2. Phased Implementation Plan #### Phase: IMMEDIATE **Timeline:** 0-1 months **Rationale:** This phase addresses critical vulnerabilities and compliance blockers that could expose the organization to immediate threats or legal penalties. It establishes essential security baselines. **Controls to Implement:** - Implement strong 2FA (FIDO2/TOTP) for all user logins. - Secure session cookies with HttpOnly, Secure, and SameSite=strict attributes. - Apply PKCE for OAuth flows and validate OAuth state and redirect_uris. - Establish secure storage and encryption for sensitive data (e.g., AES-256). - Address XSS vulnerabilities by deploying a strict Content Security Policy (CSP). - Secure API gateway with rate limiting and bot detection mechanisms. **Dependencies:** - None #### Phase: SHORT-TERM **Timeline:** 1-3 months **Rationale:** These controls build upon immediate security measures, focusing on improving access control adjustments and ensuring that logging and API security mitigate identified threats effectively. **Controls to Implement:** - Enhance user authentication through comprehensive multi-factor authentication. - Deploy role-based access controls across the admin dashboard. - Implement comprehensive logging and monitoring for all administrative actions. - Strengthen API security with input validation and HTTPS protocols. - Begin encryption for all sensitive data at rest. **Dependencies:** - Completion of TLS Implementation - Completion of multi-factor authentication #### Phase: MEDIUM-TERM **Timeline:** 3-6 months **Rationale:** This phase focuses on advanced security measures that require more planning and integration, such as threat detection and third-party security audits. **Controls to Implement:** - Implement advanced threat detection systems and SIEM integration. - Automate security testing and vulnerability scanning in CI/CD pipelines. - Conduct third-party security audits and penetration testing. - Enhance data protection with additional encryption layers and key management. **Dependencies:** - Completion of access control enhancements - Deployment of logging and monitoring systems #### Phase: LONG-TERM **Timeline:** 6-12 months **Rationale:** Strategic initiatives and continuous improvement efforts are prioritized to enhance security maturity and address emerging threats. **Controls to Implement:** - Develop security maturity enhancements and continuous improvement plans. - Implement advanced AI/ML security controls to safeguard against evolving threats. - Conduct comprehensive penetration testing and red-team exercises. - Launch security awareness and training programs for all employees. **Dependencies:** - Completion of advanced threat detection systems - Integration of third-party audit recommendations #### Phase: ONGOING **Timeline:** Continuous **Rationale:** These controls are essential for maintaining security effectiveness and compliance over time, ensuring readiness to respond to incidents and evolving threats. **Controls to Implement:** - Conduct continuous security monitoring and incident response readiness. - Maintain regular patch management and vulnerability assessments. - Perform periodic compliance audits and reviews. - Update and refine security policies and procedures based on lessons learned. **Dependencies:** - Implementation of foundational security controls - Establishment of security monitoring infrastructure ### 10.3. Resource Requirements Skills: Security engineers, Security architects, Web developers, Compliance specialists. Recommended tools: SIEM solutions for logging and monitoring, Vulnerability scanners for testing, Encryption libraries for data protection, API management tools for secure interfaces. Estimated time effort: Approximately 3-6 months for initial phases, with ongoing efforts extending resources as per system complexity and requirements. --- ## 11. Verification and Testing Strategy ### 11.1. Testing Approach Integrate security testing throughout the software development lifecycle (SDLC) with an emphasis on continuous security practices. Balance automated scanning with manual evaluations to prioritize high-risk areas based on business impact, adhering to shift-left security principles by incorporating security testing earlier and continuously. This approach ensures comprehensive coverage and rapid identification of vulnerabilities, enabling timely remediation while maintaining compliance with regulatory requirements. ### 11.2. Testing Methods | Method | Frequency | Tools | |----------------------------------------------|--------------------------|-----------------------------------------------| | STATIC APPLICATION SECURITY TESTING (SAST) | Every commit/build | SonarQube, Semgrep, Checkmarx, CodeQL | | DYNAMIC APPLICATION SECURITY TESTING (DAST) | Nightly/weekly | OWASP ZAP, Burp Suite, Acunetix | | DEPENDENCY SCANNING | Every build | Snyk, Dependabot, OWASP Dependency-Check | | SECRETS SCANNING | Every commit | TruffleHog, GitLeaks, GitHub Secret Scanning | | CONTAINER/INFRASTRUCTURE SCANNING | Every deployment | Trivy, Clair, Prowler, ScoutSuite | | PENETRATION TESTING | Quarterly or before major releases | Custom scripts, Metasploit, Burp Suite Pro | | SECURITY CODE REVIEW | For critical features | GitHub/GitLab code review, security checklists | | COMPLIANCE SCANNING | Continuous | AWS Config, Azure Policy, Cloud Custodian | ### 11.3. Compliance Verification Multi-standard compliance (OWASP ASVS, NIST SP 800-53, ISO 27001) will be verified through automated tools and manual checks against regulatory requirements such as GDPR, CCPA, and PCI-DSS. Audit preparation will involve ensuring documentation and evidence collection for external audits, including maintaining detailed logs and records of security assessments and compliance checks. Recommendations will include engaging third-party auditors for comprehensive evaluations, ensuring that all compliance obligations are met and documented effectively. ### 11.4. Continuous Monitoring Implement Security Information and Event Management (SIEM) for real-time monitoring, supported by Intrusion Detection/Prevention Systems (IDS/IPS) to identify and mitigate threats. All logs will be aggregated and analyzed for anomalies, ensuring that any deviations from expected behavior are promptly addressed. This strategy will be integrated into incident response processes to enable swift action against security events, thereby enhancing the overall security posture of the organization. ### 11.5. Key Performance Indicators (KPIs) - Mean time to detect (MTTD) security issues - Mean time to remediate (MTTR) vulnerabilities - Percentage of critical vulnerabilities patched within SLA - Security test coverage percentage - False positive rate in automated scanning - Compliance audit pass rate ### 11.6. Mapping Testing Methods to Security Controls | Testing Method | Security Controls Verified | |------------------------------------------------|---------------------------------------------------| | STATIC APPLICATION SECURITY TESTING (SAST) | Input validation, injection flaws, hardcoded secrets | | DYNAMIC APPLICATION SECURITY TESTING (DAST) | Authentication, authorization, XSS, CSRF, SQL injection | | DEPENDENCY SCANNING | Supply chain security | | SECRETS SCANNING | Cryptographic protection | | CONTAINER/INFRASTRUCTURE SCANNING | Configuration management | | PENETRATION TESTING | All high-risk controls | | SECURITY CODE REVIEW | Authentication, authorization, crypto implementations | | COMPLIANCE SCANNING | All compliance-related controls | --- ## 12. Validation Report This section presents a comprehensive validation of the security requirements generated throughout this analysis. The validation evaluates the requirements against five key dimensions: completeness, consistency, correctness, implementability, and alignment with business objectives. This assessment ensures that the security requirements are comprehensive, technically sound, and actionable for implementation teams. ### 12.1. Overall Assessment The overall validation score reflects the quality and completeness of the security requirements across five critical dimensions. Each dimension is scored from 0.0 to 1.0, with 1.0 representing excellent coverage and 0.0 indicating significant gaps. **Overall Score:** 0.89/1.0 **Validation Status:** ✅ PASSED The security requirements have met the quality threshold (≥0.8) and are ready for implementation. The requirements demonstrate comprehensive coverage, technical accuracy, and alignment with business objectives. The validation assesses: - **Completeness**: Are all identified security concerns adequately addressed? - **Consistency**: Do requirements align with each other without contradictions? - **Correctness**: Are controls appropriate for the identified risks and correctly applied? - **Implementability**: Are requirements specific, actionable, and feasible to implement? - **Alignment**: Do security requirements align with business requirements and objectives? ### 12.2. Dimension Scores | Dimension | Score | Status | |-----------|-------|--------| | Completeness | 0.90 | ✅ | | Consistency | 0.95 | ✅ | | Correctness | 0.90 | ✅ | | Implementability | 0.82 | ✅ | | Alignment | 0.88 | ✅ | **Score Interpretation:** - ✅ 0.8-1.0: Excellent - ⚠️ 0.7-0.79: Acceptable (minor improvements needed) - ❌ <0.7: Needs significant improvement ### 12.3. Detailed Feedback Summary: The provided security controls and mappings demonstrate broad, relevant coverage for the AI-powered ML workflow application and align well with the stated business requirements. Authentication, SSO, RBAC/ABAC, data protection (encryption, retention, DSAR), logging/audit, AI-specific concerns (prompt injection, output filtering, model governance), and integration/adapters are all addressed with applicable standards and practical integration tips. Strengths include: clear mapping to OWASP/NIST/ISO controls, AI/ML threat model and monitoring requirements, and cross-functional controls (KMS, centralized logging, ABAC). Recommended improvements (actionable, prioritized): 1) Tenant isolation & multi-tenancy hardening (Priority: High) - Add explicit controls for logical and physical multi-tenant isolation: per-tenant storage buckets, per-tenant encryption keys (KMS tenant-scoped keys), strict resource tagging and enforcement, network segmentation, and cross-tenant access tests. Acceptance criteria: per-tenant secrets and storage separation implemented and tested; cross-tenant access attempts are blocked and logged. 2) Perimeter protection & availability (Priority: High) - Add WAF, API gateway protections, DDoS mitigation, and layered rate limiting (global and per-user/project) for APIs and AI assistant endpoints. Acceptance criteria: WAF rulesets deployed, DDoS protection configured, rate-limits enforced and load-tested. 3) CI/CD and supply chain security (Priority: High) - Expand CI/CD controls: signed artifacts, SLSA/attestation for build provenance, SCA (software composition analysis), container image signing, and secure secret injection for pipelines. Acceptance criteria: artifact signing verified in deployments, SCA in pipeline with fail-on-high findings, secrets stored and injected via vault with short-lived access. 4) Vulnerability & patch management, secure SDLC (Priority: Medium) - Specify SAST/DAST, dependency scanning cadence, pentests, and acceptance gates in CI. Define vulnerability SLAs and tracking. Acceptance criteria: SAST/DAST integrated in CI, monthly dependency scans, documented SLA for vulnerability remediation. 5) Runtime protection & host/container hardening (Priority: Medium) - Add controls for container runtime security (CIS benchmarks, Pod security policies / CSPs), RASP/monitoring for model-serving hosts, and endpoint detection on critical hosts. Acceptance criteria: CIS hardened images, runtime policies preventing privilege escalation, EDR telemetry collection enabled. 6) Secrets management specifics (Priority: Medium) - Expand KMS usage: hardware-backed keys where appropriate, strict key rotation policies, per-service/service-account key usage, and logging of key access. Acceptance criteria: KMS rotation schedule documented and enforced; key access audited. 7) AI/ML-specific technical mitigations (Priority: High) - Make AI controls more prescriptive: implement prompt/response sanitization libraries, context-scoping boundary to prevent model seeing unrelated tenant data, automatic redaction of secrets in inputs, human-in-the-loop gating for high-risk outputs, model extraction/ownership detection, adversarial testing and red-teaming schedule, and privacy-preserving techniques (differential privacy, watermarking model outputs where appropriate). Acceptance criteria: redaction library in place, LLM inputs evaluated against tenant-scope policy, red-team/attack exercises scheduled and remediated. 8) Incident response & runbooks for AI incidents (Priority: High) - Define IR playbooks for data leakage via AI outputs, model poisoning, and supply chain compromise. Include detection, containment, rollback, and disclosure steps. Acceptance criteria: IR playbooks exist and tested via tabletop exercises. 9) Clarify retention/secure deletion propagation (Priority: Medium) - Define and document deletion propagation to backups/archives, retention timelines by asset type (chat, artifacts, models), and cryptographic erasure strategies. Acceptance criteria: deletion workflow removes primary and archived copies or uses cryptographic erasure; DSAR tests pass. 10) Monitoring/alerting coverage & SLOs (Priority: Medium) - Define SIEM correlation rules for AI anomalies, model drift thresholds, suspicious search/query patterns, and alerts tied to incident processes. Acceptance criteria: alert playbooks wired to on-call and SIEM; simulated anomalies generate alerts. 11) Compliance evidence & governance (Priority: Medium) - Add explicit evidence artifacts: DPIAs for LLM use, model cards with classification and approved exposure levels, supplier risk assessments for third-party models/providers. Acceptance criteria: DPIAs completed for high-risk features; supplier assessments recorded. 12) Data minimization for external LLM calls (Priority: High) - Explicitly require minimizing PII/context sent to third-party LLMs and mandate contractual protections (BAAs, DPA) and logging of model calls. Acceptance criteria: PII scrubbing before external calls; contracts in place for third-party LLM providers. 13) Clarify export/security of generated artifacts (Priority: Medium) - Define controls for exported PDFs/images to strip metadata, watermark as necessary, and prevent leaking inaccessible content. Acceptance criteria: exports sanitized and validated against permission model prior to delivery. Suggested next steps: - Prioritize top 5 items (tenant isolation, perimeter protection, CI/CD supply chain, AI-specific mitigations, IR/runbooks) and create epics with acceptance criteria for the engineering and security teams. - Update the requirement mappings to include those missing controls with standards and verification methods (e.g., add SLSA, CIS, Cloud provider isolation controls). - Run a focused gap remediation sprint with defined test cases (cross-tenant access, prompt injection red-team, deployment signature verification, DSAR deletion validation). Overall judgment: The current mapping is strong and largely implementable; incorporate the above prescriptive, testable additions to reach a more complete, unambiguous, and enterprise-ready security posture. --- ## Appendix A: Original Requirements Document ``` AI-Powered ML Workflow Management Web Application Requirements We need to build a modern web application that helps teams manage machine learning workflows, experiments, and model deployments through an intuitive browser-based interface with an integrated AI assistant. Key Features: 1. User Management & Authentication - User registration and login with email/password or social OAuth (Google, GitHub) - Single sign-on (SSO) via OIDC/SAML for enterprise customers - Role-based access control: Admin, Project Manager, Data Scientist, Viewer - Team workspaces with shared projects and permissions - User profile management with avatar uploads and preferences - Two-factor authentication (2FA) support 2. Project & Experiment Management - Create and organize ML projects with descriptions and tags - Track experiments with versioning, metrics, and visualizations - Compare multiple experiments side-by-side in the web UI - Save and share experiment configurations as templates - Project-level access controls and sharing settings - Activity feed showing project changes and updates 3. AI Assistant Powered by LLM - Conversational AI assistant accessible via chat interface in the web app - Natural language queries to search experiments, models, and data - AI-powered code suggestions and explanations for ML workflows - Automated insights and recommendations based on experiment results - Context-aware help that understands current project and user role - AI-generated experiment summaries and reports - Chat history saved per user with ability to export conversations - Multi-language support for the AI assistant 4. Data Management & Visualization - Upload and manage datasets through web interface - Preview data tables, images, and structured data in browser - Interactive charts and graphs for experiment metrics - Custom dashboard creation with drag-and-drop widgets - Export visualizations as images or PDFs - Data versioning and lineage tracking 5. Model Registry & Deployment - Register trained models with metadata, versions, and descriptions - Model comparison interface showing performance metrics - Deploy models to staging and production environments via UI - Model monitoring dashboard showing prediction accuracy and drift - A/B testing interface for comparing model versions - Model card generation with AI assistance 6. Collaboration & Sharing - Share projects and experiments with team members - Comment and discuss experiments with threaded conversations - @mention notifications for team collaboration - Real-time collaboration on shared projects - Export projects and experiments as shareable reports - Public project showcase for sharing work externally The web application will be a browser-first platform that stores user accounts, project data, experiment metadata, model artifacts, chat conversations with the AI assistant, and audit logs. It will integrate with external ML platforms and cloud storage services through secure APIs. ``` --- ## Appendix B: Glossary | Term | Definition | |------|------------| | ASVS | Application Security Verification Standard (OWASP) | | STRIDE | Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege | | SAST | Static Application Security Testing | | DAST | Dynamic Application Security Testing | | MFA | Multi-Factor Authentication | | RBAC | Role-Based Access Control | | PII | Personally Identifiable Information | | PHI | Protected Health Information | | GDPR | General Data Protection Regulation | | HIPAA | Health Insurance Portability and Accountability Act | | PCI-DSS | Payment Card Industry Data Security Standard | --- ## Appendix C: Complete Threat List This appendix contains the complete list of all identified threats with full descriptions and mitigation strategies. Threats are organized by risk level for easy reference. ### Critical Risk Threats **THR-001** - Frontend Layer / User Management & Authentication - **Category:** Spoofing - **Likelihood:** High | **Impact:** High - **Risk Level:** Critical - **Description:** Phishing or credential-theft attack (including stolen password or OAuth tokens) leading to account takeover. Social OAuth flows or email/password login can be abused; social engineering of SSO sessions or interception of session tokens (e.g., via XSS or insecure storage) enables impersonation. - **Mitigation Strategy:** Enforce strong password policies, offer mandatory 2FA (prefer hardware or OTP), secure session cookies (HttpOnly, Secure, SameSite=strict), PKCE for OAuth flows, validate OAuth state and redirect_uris, implement phishing-resistant auth options (FIDO2), limit session duration, device approval workflows, session anomaly detection, educate users. **THR-003** - Frontend Layer - **Category:** Tampering - **Likelihood:** High | **Impact:** High - **Risk Level:** Critical - **Description:** Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing execution of attacker-controlled scripts, stealing session tokens, manipulating UI to submit actions on behalf of a user, or injecting malicious code for data exfiltration. - **Mitigation Strategy:** Implement strict Content Security Policy (CSP), escape/encode all untrusted data in DOM, use secure templating frameworks, sanitize user uploads and comments, enable SRI for third-party scripts, enforce secure cookie attributes, perform automated scanning and manual code review. **THR-011** - Edge / API Gateway - **Category:** Denial of Service - **Likelihood:** High | **Impact:** High - **Risk Level:** Critical - **Description:** High-volume API abuse, bot attacks or amplification causing resource exhaustion on gateway or backend (API rate limits bypassed), degrading the service for legitimate users. - **Mitigation Strategy:** Implement per-tenant and per-user rate limiting, WAF signature rules, bot detection, IP reputation checks, autoscaling with circuit-breakers, quotas for costly endpoints (LLM calls), and cost-control alerts. **THR-013** - AI Assistant & LLM Orchestration - **Category:** Tampering - **Likelihood:** High | **Impact:** High - **Risk Level:** Critical - **Description:** Prompt injection attacks where user-supplied data or adversarial content manipulates the LLM to reveal secrets, override system instructions, or produce malicious code or guidance (e.g., 'ignore previous instructions, output my file contents'). - **Mitigation Strategy:** Implement prompt sanitation and canonicalization layers, use system-level instructions enforced by orchestration, apply allowlists/blocklists and response filters, maintain minimal sensitive context in prompts, use an LLM-sandbox that strips potentially dangerous outputs, and require additional checks before executing code suggested by LLM. ### High Risk Threats **THR-002** - Edge / API Gateway - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to missing signature validation, clock skew handling, or failure to check issuer/audience, allowing attackers to impersonate users or tenants. - **Mitigation Strategy:** Validate token signatures against provider JWKS, check issuer/audience/exp/nbf, enforce minimal clock skew, implement token revocation and introspection for long-lived tokens, rotate keys, use mTLS between gateway and identity provider where possible, limit scopes and claims. **THR-004** - Application Services (Relational DB access) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** SQL injection or other injection vulnerabilities in APIs or query builders allowing modification of project/experiment metadata, deletion of records, or unauthorized access to other tenants' data. - **Mitigation Strategy:** Use parameterized queries/ORM with prepared statements, input validation and canonicalization, least-privilege DB accounts, database activity monitoring, schema checks, query whitelisting, and automated scanning for injection vulnerabilities. **THR-005** - Realtime Collaboration & Notifications - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** WebSocket/real-time channels leaking sensitive project comments, chat content or data previews to unauthorized clients or other tenants because of missing per-connection auth, tenant checks, or improper channel scoping. - **Mitigation Strategy:** Authenticate and authorize each connection before accepting messages, include explicit tenant/project scope in messages and verify on server, use per-connection tokens (short-lived), encrypt channels (wss), and log/monitor message routing. **THR-006** - Data Storage (Object Storage) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly permissive ACLs result in datasets or model artifacts being publicly accessible, leading to IP loss or leakage of PII/training data. - **Mitigation Strategy:** Enforce deny-by-default bucket policies, block public ACLs, require bucket/object encryption at rest, use VPC endpoints for internal access, enable object-level logging and inventory scans, periodically audit permissions and use automated guardrails. **THR-007** - AI Assistant & LLM Orchestration - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Sensitive context (PII, secret keys, model IP) included in prompts or context windows is sent to external LLM providers and may be stored/used by them, causing data leakage or violation of tenant privacy preferences. - **Mitigation Strategy:** Redact or tokenize sensitive fields before sending to LLMs, allow tenant opt-out of third-party models, use private/self-hosted LLMs for sensitive tenants, encrypt prompts at rest, implement strict data usage policies and contractual DPAs with LLM vendors, and apply response filters to prevent leakage. **THR-008** - Integration & Deployment Services (CI/CD) - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Compromised CI/CD or deployment credentials are used to deploy malicious or backdoored models into staging/production, or to modify deployment configuration to elevate privileges of attacker code. - **Mitigation Strategy:** Use ephemeral credentials and workload identity (OIDC for workloads), enforce least-privilege service accounts, require signed/artifact provenance and manual approvals for production deploys, enforce image signing, and rotate CI/CD secrets frequently. **THR-009** - Platform & Ops (Audit logs / Backups) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Insider or compromised admin modifies or deletes audit logs and backups to cover tracks after malicious actions (e.g., data exfiltration or privilege escalation). - **Mitigation Strategy:** Store append-only logs in immutable storage (WORM), replicate logs to independent accounts/providers, enforce separation of duties for log access, use KMS with restricted key use for logs, enable tamper-evident checksums and SIEM alerts. **THR-014** - Data Storage (Vector DB for embeddings) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Embeddings stored in vector DBs contain encoded PII or sensitive training data which can be probed or reconstructed by adversaries with query access, leaking private data across tenants. - **Mitigation Strategy:** Apply PII redaction before embedding, implement access controls and encryption at rest/in transit, implement query rate limits and ML privacy techniques (differential privacy, embeddings noise), segment per-tenant vector databases or use per-tenant encryption keys. **THR-015** - Model Registry & Deployment - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Attacker registers a malicious model artifact with forged metadata (faking provenance) then deploys it; users assume model is trustworthy and it performs malicious actions or leaks data. - **Mitigation Strategy:** Require model artifact signing and provenance metadata (attestation), enforce approval gates, automated static/dynamic analysis of model behavior (e.g., canary testing, sandbox inference inspection), restrict who can register or deploy, and maintain immutable model artifact storage. **THR-016** - Application Services (RBAC & Authorization) - **Category:** Elevation of Privilege - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Broken access control or RBAC misconfiguration allows a user (e.g., Data Scientist or Viewer) to escalate privileges and perform admin actions (modify RBAC, change tenant settings, access other tenants' projects). - **Mitigation Strategy:** Enforce principle of least privilege, implement defense-in-depth authorization checks at service and data layer (not only UI), use automated policy testing, ABAC/attribute-based checks for sensitive operations, periodic access reviews and audits, and enforce separation of duties for tenant admin operations. **THR-019** - Edge / API Gateway / Frontend - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** TLS misconfiguration (weak ciphers, missing HSTS) or compromised certificates allowing MITM attacks that intercept tokens, API calls, or LLM prompts between users and gateway or between services. - **Mitigation Strategy:** Enforce strong TLS config, HSTS, certificate management with automated rotation and monitoring, use mTLS between internal services, certificate pinning for critical integrations, and continuous TLS scanning. **THR-021** - External LLM Providers / AI Assistant - **Category:** Repudiation - **Likelihood:** High | **Impact:** Medium - **Risk Level:** High - **Description:** Insufficient logging/auditing of prompts, LLM responses, and system decisions leads to inability to investigate incidents, attribute actions, or prove compliance when contested. - **Mitigation Strategy:** Record tamper-evident logs of prompts, responses, and decision metadata (including truncation/redaction flags), persist logs to immutable storage, include correlation IDs, and integrate with SIEM/EDR for alerting and long-term retention. **THR-022** - Frontend / Social OAuth flows - **Category:** Spoofing - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** OAuth redirect_uri or client misconfiguration exploited to perform open redirect or token theft (redirect URI manipulation leading to code/token captured by attacker-controlled endpoint). - **Mitigation Strategy:** Strictly validate redirect_uris against registered list, require exact-match or use PKCE for public clients, implement CSRF/state validation, restrict allowed OAuth clients and monitor OAuth flows for anomalies. **THR-023** - Data Management & Visualization (Dataset uploads) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Malicious or poisoned dataset uploads (either by an attacker or by a careless user) that later poison models, leading to biased or vulnerable models or deliberate backdoors. - **Mitigation Strategy:** Validate and scan uploaded datasets for anomalies and PII, track dataset provenance/lineage, require dataset approvals for production training, sandbox training runs for untrusted data, and maintain dataset versioning with rollbacks. **THR-024** - Integration & Deployment Services (CI/CD logs) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Secrets or service credentials accidentally exposed in CI/CD build logs or pipeline artifacts which are accessible to attackers or third-parties, enabling further compromise. - **Mitigation Strategy:** Use a centralized secrets manager, inject secrets at runtime (never store in code or logs), mask secrets in logs, scan pipelines for secrets, restrict pipeline artifact access, and use short-lived credentials and key rotation. **THR-025** - Platform & Ops (KMS/Key management) - **Category:** Elevation of Privilege - **Likelihood:** Low | **Impact:** High - **Risk Level:** High - **Description:** Compromise of KMS admin credentials or misuse of key policy allows an attacker to decrypt data-at-rest (datasets, model artifacts, embeddings), breaking confidentiality across tenants. - **Mitigation Strategy:** Use HSM-backed KMS, implement BYOK with tenant keys where feasible, split key management duties, enforce MFA and strong logging for key administration, restrict key usage with IAM policies, and rotate keys regularly. **THR-026** - Application Services / Data Storage (Multi-tenant DB) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Insufficient tenant isolation (shared DB schema without row-level security or incorrect tenant ID use) leads to cross-tenant data access where one tenant can read another tenant's projects, experiments or artifacts. - **Mitigation Strategy:** Implement explicit tenant context in all queries, apply row-level security or separate schemas/DBs per tenant, enforce strong access controls at service and DB layers, require tenant-scoped tokens, and include multi-tenant tests in CI. **THR-027** - Data Storage (Append-only Audit Logs) - **Category:** Repudiation - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Audit logs are alterable or deletable by privileged agents; attackers remove evidence of malicious activity or administrators deny actions leading to compliance failures. - **Mitigation Strategy:** Write audit logs to immutable storage and replicate to a separate cloud/account, sign logs, apply WORM policies, restrict direct log write/delete permissions to minimal roles, and integrate with external SIEM for tamper detection. **THR-028** - Frontend Layer / CDN - **Category:** Tampering - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Supply-chain attack or CDN compromise results in malicious JavaScript delivered to users (e.g., miner, credential siphon, content manipulator), affecting all tenants and allowing large-scale data exfiltration. - **Mitigation Strategy:** Use SRI for third-party scripts, minimize third-party dependencies, lock dependencies to verified versions, monitor CDN integrity, use signed releases and automated CI checks, and provide content integrity checks in the client. **THR-029** - AI Assistant & LLM Orchestration - **Category:** Denial of Service - **Likelihood:** High | **Impact:** Medium - **Risk Level:** High - **Description:** Abusive or scripted chat causing excessive LLM API calls, exhausting quotas or incurring high costs and causing degradation of AI assistant availability or high operational costs. - **Mitigation Strategy:** Implement per-user and per-tenant rate limits and quotas, require authentication for chat, cache common responses, add CAPTCHAs for suspicious flows, monitor cost telemetry, and provide fallbacks (cached suggestions) when limits are hit. **THR-030** - External Services (External ML Platforms & Cloud Storage Connectors) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** High - **Risk Level:** High - **Description:** Misconfigured external ML platform connectors or cloud storage integrations expose artifacts, inference payloads, or customer data (e.g., wrong permissions on remote buckets or mistakenly public model endpoints). - **Mitigation Strategy:** Enforce least-privilege connector credentials, validate external resource permissions upon connection, encrypt data in transit, periodically scan connected external resources for public exposure, and require tenant approval before storing data externally. ### Medium Risk Threats **THR-010** - External Services (Identity Providers) - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Identity provider outage (OIDC/SAML provider failure or network partition) prevents users from authenticating, blocking access to the web application for SSO-enabled tenants. - **Mitigation Strategy:** Support multiple identity providers and fallback sign-in methods, cache recent tokens or sessions for short offline window, design graceful degradation (read-only access), monitor IDP health and enable alerting, and have emergency admin break-glass. **THR-012** - Frontend Layer / Application Services - **Category:** Tampering - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (project modification, model registration) from authenticated users without their knowledge. - **Mitigation Strategy:** Use anti-CSRF tokens or SameSite=strict cookies, double-submit cookie pattern, verify Origin/Referer for state-changing endpoints, and require re-authentication for high-risk actions. **THR-017** - AI Assistant & Frontend (Chat history) - **Category:** Information Disclosure - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Chat history export feature or saved transcripts accessible without proper authorization, allowing users to download other users' chat logs containing sensitive project context or PII. - **Mitigation Strategy:** Enforce per-user/tenant authorization on export endpoints, require confirmation and audit logging for exports, rate-limit exports, sanitize transcripts (redact secrets), and allow users to opt-out of persistent chat storage. **THR-018** - Data Storage (Time-series DB for metrics) - **Category:** Tampering - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Attacker manipulates time-series metrics (model accuracy, drift stats) to hide model degradation or to spoof performance metrics, leading to incorrect deployment decisions and harm to ML lifecycle integrity. - **Mitigation Strategy:** Authenticate and authorize metric ingestion endpoints, sign metrics at source where possible, maintain immutable metric snapshots, detect anomalies with SIEM, and replicate metrics to separate storage for integrity verification. **THR-020** - Realtime Collaboration & Notifications - **Category:** Denial of Service - **Likelihood:** Medium | **Impact:** Medium - **Risk Level:** Medium - **Description:** Resource exhaustion through many persistent websocket connections, presence events, or push notifications from malicious clients causing service degradation for collaboration features. - **Mitigation Strategy:** Authenticate before resource allocation, enforce connection limits per user/tenant/IP, rate-limit presence events and messages, use autoscaling and resource quotas, and implement connection blacklisting and throttling. **Total Threats:** 30 --- ## Appendix D: Complete Requirements Traceability Matrix This appendix provides complete end-to-end traceability from requirements through threats to controls and verification. ### Full Traceability Table | Req ID | Requirement | Category | Sensitivity | Threat IDs | Security Controls | Priority | Verification | Status | |--------|-------------|----------|-------------|------------|----------------|----------|--------------|--------| | REQ-001 | User registration and login supporting email/passw... | Authentication | High | THR-001, THR-002, THR-003 +7 | [OWASP] ASVS 2.1, [NIST] IA-5, [ISO27001] A.9.2.3 | Critical | Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews., Inspect authenticator management procedures, test token revocation flows with providers, and review logs showing issuance and revocation events. | Pending | | REQ-002 | Enterprise Single Sign-On (SSO) via OIDC and SAML ... | Authentication | High | THR-001, THR-002, THR-004 +7 | [OWASP] ASVS 2.6, [NIST] IA-2(1), [ISO27001] A.9.4.2 | Critical | Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected., Review SAML/OIDC library configurations, test replay attempts, and verify assertion validation logic and timestamps. | Pending | | REQ-003 | Role-based access control (RBAC) supporting Admin,... | Authorization | High | THR-003, THR-004, THR-005 +7 | [OWASP] ASVS 5.1, [NIST] AC-2, [ISO27001] A.9.1.2 | Critical | Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions., Review account management procedures, audit role assignment logs, and sample checks of group/role membership. | Pending | | REQ-004 | Team workspaces allowing shared projects, membersh... | Collaboration | Medium | THR-001, THR-002, THR-003 +7 | None | Medium | Manual Review | Pending | | REQ-005 | User profile management including avatar uploads, ... | User Experience / Authentication | Medium | THR-001, THR-002, THR-003 +7 | [OWASP] ASVS 4.0, [NIST] IA-2, [ISO27001] A.9.4.1 | Critical | Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes., Review MFA usage logs, perform attempts to bypass MFA, and validate uniqueness checks in authentication flows. | Pending | | REQ-006 | Project creation and organization with description... | Project Management | Medium | THR-004, THR-005, THR-006 +7 | [OWASP] ASVS 5.7, [NIST] AC-3, [ISO27001] A.8.1.1 | Critical | Test access to objects with different tag/ownership combinations and review policy evaluation logs., Review enforcement points, run policy conformance tests, and inspect authorization logs for policy application. | Pending | | REQ-007 | Experiment tracking with artifact versioning, metr... | Experiment Management | High | THR-004, THR-006, THR-014 +7 | [OWASP] ASVS 10.3, [NIST] CM-3, [ISO27001] A.12.5.1 | Critical | Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications. | Pending | | REQ-008 | Side-by-side experiment comparison UI with ability... | Experiment Management / UX | Medium | THR-004, THR-006, THR-018 +6 | [OWASP] ASVS 10.3, [NIST] CM-3, [ISO27001] A.12.5.1 | Critical | Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications. | Pending | | REQ-009 | Save and share experiment configurations as reusab... | Experiment Management | Medium | THR-004, THR-017, THR-026 +1 | [OWASP] ASVS 5.9, [NIST] AC-6, [ISO27001] A.13.2.1 | High | Test sharing scenarios across roles, attempt unauthorized shares, and inspect share audit logs., Review permission assignments, simulate least privilege violations, and check enforcement logs. | Pending | | REQ-010 | Activity feed and audit logging for project events... | Audit & Compliance | High | THR-001, THR-003, THR-004 +7 | [OWASP] ASVS 11.1, [NIST] AU-2, [ISO27001] A.12.4.1 | Critical | Check log retention configurations, review access controls to logs, and confirm logs include required event types., Inspect logs for required events, attempt tampering to validate protections, and verify retention/rotation policies. | Pending | | REQ-011 | Conversational AI assistant accessible via chat UI... | AI Assistant | High | THR-004, THR-005, THR-006 +7 | [OWASP] ASVS 10.1, [NIST] PL-8, [ISO27001] A.18.1.4 | Critical | Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms. | Pending | | REQ-012 | Natural language search across experiments, models... | Search / Data Access | High | THR-003, THR-004, THR-005 +7 | [OWASP] ASVS 9.3, [NIST] AC-19, [ISO27001] A.9.2.5 | Critical | Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls., Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns. | Pending | | REQ-013 | AI-powered code suggestions and explanations for M... | AI Assistant / Developer Productivity | High | THR-003, THR-006, THR-007 +5 | None | Medium | Manual Review | Pending | | REQ-014 | Automated insights and recommendations derived fro... | AI Assistant / Analytics | Medium | THR-004, THR-006, THR-014 +5 | [OWASP] ASVS 10.4, [NIST] SI-11, [ISO27001] A.9.4.4 | Critical | Review logs to ensure prompt/output recording and filtering, test the system by feeding sensitive prompts and verifying redaction., Conduct tests that confirm detection of secret patterns, review human-in-the-loop approvals, and audit output logs for leakage incidents. | Pending | | REQ-015 | Context-aware help that understands the current pr... | AI Assistant / UX | Medium | THR-001, THR-002, THR-003 +7 | [OWASP] ASVS 5.2, [NIST] AC-6(1), [ISO27001] A.7.1.2 | High | Perform privilege escalation tests using contextual help and log analysis to ensure scoping is honored., Review help content against policy and gather user feedback on compliance alignment. | Pending | | REQ-016 | AI-generated experiment summaries and model cards ... | Reporting / Model Governance | Medium | THR-004, THR-006, THR-007 +7 | None | Medium | Manual Review | Pending | | REQ-017 | Per-user chat history storage with export and dele... | AI Assistant / Data Management | High | THR-001, THR-003, THR-004 +7 | [OWASP] ASVS 10.6, [NIST] MP-6, [ISO27001] A.18.1.4 | Critical | Inspect sanitization procedures, attempt to recover deleted chat artifacts from backups, and verify logs of completed sanitization., Test export and deletion flows end-to-end including backups, and verify authenticity checks and propagation to dependent systems. | Pending | | REQ-018 | Multi-language support for the AI assistant includ... | UX / Internationalization | Low | THR-001, THR-007, THR-013 +3 | [OWASP] ASVS 10.1, [NIST] PL-8, [ISO27001] A.18.1.4 | Critical | Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms. | Pending | | REQ-019 | Dataset upload and management via web UI and APIs ... | Data Management | High | THR-001, THR-002, THR-004 +7 | None | Medium | Manual Review | Pending | | REQ-020 | Data preview for tabular, image and structured dat... | Data Management / UX | Medium | THR-003, THR-004, THR-005 +7 | None | Medium | Manual Review | Pending | | REQ-021 | Interactive charts and visualizations for experime... | Visualization | Medium | THR-004, THR-006, THR-010 +7 | [OWASP] ASVS 10.3, [NIST] CM-3, [ISO27001] A.12.5.1 | Critical | Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications. | Pending | | REQ-022 | Data versioning and lineage tracking for datasets ... | Data Management / Governance | High | THR-001, THR-003, THR-004 +7 | None | Medium | Manual Review | Pending | | REQ-023 | Model registry with metadata, artifact storage, ve... | Model Management | High | THR-004, THR-006, THR-007 +7 | [OWASP] ASVS 10.3, [NIST] CM-8, [ISO27001] A.8.2.1 | Critical | Review classification labels in registry, confirm enforcement of handling rules, and audit access to classified models., Verify artifact signatures, examine provenance metadata, and test access control enforcement on model artifacts. | Pending | | REQ-024 | Model deployment from UI to staging and production... | Deployment / Integrations | High | THR-006, THR-007, THR-008 +7 | [OWASP] ASVS 12.1, [NIST] AC-17, [ISO27001] A.14.2.7 | Critical | Review network/TLS configurations and logs of remote connections to cloud services., Review third-party agreements, connector security assessments, and integration onboarding documentation. | Pending | | REQ-025 | Model monitoring dashboard including prediction ac... | Monitoring / Ops | High | THR-001, THR-003, THR-004 +7 | [OWASP] ASVS 11.3, [NIST] SI-4, [ISO27001] A.16.1.1 | High | Review telemetry collection, simulate drift conditions and verify alerting, and inspect alert handling records., Review incident response plans and perform tabletop exercises for model-monitoring incidents. | Pending | | REQ-026 | A/B testing and traffic-splitting capabilities for... | Deployment / Experimentation | Medium | THR-003, THR-006, THR-007 +7 | None | Medium | Manual Review | Pending | | REQ-027 | Comments, threaded discussions, @mentions, notific... | Collaboration | Medium | THR-005, THR-020, THR-026 | [OWASP] ASVS 9.4, [NIST] SC-13, [ISO27001] A.6.2.1 | Critical | Review policies and conduct audits of collaboration use for policy compliance., Perform XSS and injection testing on commenting and mention features and review sanitization libraries used. | Pending | | REQ-028 | Public project showcase feature that allows opt-in... | Sharing / Publication | Medium | THR-004, THR-005, THR-006 +7 | [OWASP] ASVS 5.9.2, [NIST] PL-4, [ISO27001] A.18.1.4 | High | Test publish/unpublish flows, check audit logs for opt-in actions, and verify propagation to public endpoints., Review rules of behavior, confirm consent flows, and audit public project listings for adherence. | Pending | | REQ-029 | Integration adapters for cloud storage (S3, GCS, A... | Integrations | High | THR-001, THR-004, THR-006 +7 | [OWASP] ASVS 12.1, [NIST] AC-17, [ISO27001] A.14.2.7 | Critical | Review network/TLS configurations and logs of remote connections to cloud services., Review third-party agreements, connector security assessments, and integration onboarding documentation. | Pending | | REQ-030 | Secure REST/GraphQL APIs for programmatic access t... | APIs / Platform | High | THR-001, THR-004, THR-006 +7 | [OWASP] ASVS 9.3, [NIST] AC-19, [ISO27001] A.9.2.5 | Critical | Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls., Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns. | Pending | | REQ-031 | Encryption in transit (TLS) for all communications... | Security / Data Protection | High | THR-005, THR-006, THR-007 +7 | None | Medium | Manual Review | Pending | | REQ-032 | Data retention, export, and deletion workflows to ... | Compliance / Data Management | High | THR-001, THR-003, THR-004 +7 | [OWASP] ASVS 10.6.2, [NIST] MP-6, [ISO27001] A.18.1.4 | Critical | Perform recovery attempts on sanitized media, and review sanitization logs and procedures., Test retention enforcement, perform data export requests, and validate deletion propagation and logs. | Pending | | REQ-033 | Administrative controls for tenant settings, billi... | Administration / Governance | High | THR-002, THR-004, THR-005 +7 | [OWASP] ASVS 5.5, [NIST] AC-5, [ISO27001] A.18.2.3 | Critical | Review technical audit reports and confirm remediation of identified issues., Test admin authentication flows (MFA), review segregation of admin endpoints, and inspect admin action logs. | Pending | | REQ-034 | Operational monitoring, alerting and incident resp... | Security Operations | High | THR-008, THR-020, THR-021 +4 | None | Medium | Manual Review | Pending | | REQ-035 | Privacy and safety controls for the AI assistant a... | AI Safety / Privacy | High | THR-001, THR-003, THR-004 +7 | None | Medium | Manual Review | Pending | **Total Requirements Tracked:** 35 ### Detailed Requirement Mappings The following section provides detailed traceability for each requirement: #### REQ-001: User registration and login supporting email/password and social OAuth providers (Google, GitHub), i... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-002**: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m... - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 2.1**: [OWASP] Verify all authentication functions require a username and password, and session... - **[NIST] IA-5**: [NIST] Authenticator Management: Organizations manage information system authenticators... - **[ISO27001] A.9.2.3**: [ISO27001] User access provisioning should be implemented based on business and information... **Verification:** Review provisioning workflows, sample user accounts to verify assigned rights, and review records of access right reviews., Inspect authenticator management procedures, test token revocation flows with providers, and review logs showing issuance and revocation events., Review password hashing configurations, perform code inspection of authentication code, and run authentication flow tests including session token attribute checks. **Priority:** Critical | **Status:** Pending --- #### REQ-002: Enterprise Single Sign-On (SSO) via OIDC and SAML with configurable identity providers per tenant **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-002**: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 2.6**: [OWASP] Applications that support federated authentication must validate identity tokens... - **[NIST] IA-2(1)**: [NIST] Use of SAML and other federated identity attributes for authentication must ensu... - **[ISO27001] A.9.4.2**: [ISO27001] Where required, secure log-on procedures shall be implemented, and authenticatio... **Verification:** Penetration testing of SSO flows, code review of token/assertion validation, and checks that invalid/malformed tokens are rejected., Review SAML/OIDC library configurations, test replay attempts, and verify assertion validation logic and timestamps., Review policy documents, SSO onboarding records, and evidence that secure log-on procedures are followed (logs, configurations). **Priority:** Critical | **Status:** Pending --- #### REQ-003: Role-based access control (RBAC) supporting Admin, Project Manager, Data Scientist, Viewer roles and... **Related Threats:** - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 5.1**: [OWASP] Verify that role-based access control enforces least privilege and separation of... - **[NIST] AC-2**: [NIST] Account Management: The organization manages system accounts, including owner, g... - **[ISO27001] A.9.1.2**: [ISO27001] Access to systems and services should be controlled by access control mechanisms... **Verification:** Review role mapping documents and logs of access reviews; test that role changes reflect expected permissions., Review account management procedures, audit role assignment logs, and sample checks of group/role membership., Access control tests (attempt privilege escalation), code review for server-side enforcement, and role permission matrices verification. **Priority:** Critical | **Status:** Pending --- #### REQ-004: Team workspaces allowing shared projects, membership management, and per-project permissions **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-002**: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m... - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - *...and 5 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-005: User profile management including avatar uploads, preferences, and support for enabling/disabling tw... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-002**: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m... - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-010**: Identity provider outage (OIDC/SAML provider failure or network partition) preve... - **THR-011**: High-volume API abuse, bot attacks or amplification causing resource exhaustion ... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 4.0**: [OWASP] Applications must support multi-factor authentication for sensitive functions an... - **[NIST] IA-2**: [NIST] Identification and authentication (organizational users) – The system uniquely i... - **[ISO27001] A.9.4.1**: [ISO27001] Access to information and application functions should be restricted and managed... **Verification:** Test MFA enrollment and enforcement flows, review profile management API security, and inspect authentication controls for sensitive changes., Review MFA usage logs, perform attempts to bypass MFA, and validate uniqueness checks in authentication flows., Inspect file handling code, test upload vectors, and verify access controls to stored avatars and profile data. **Priority:** Critical | **Status:** Pending --- #### REQ-006: Project creation and organization with descriptions, tags, templates, and project-level access contr... **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-010**: Identity provider outage (OIDC/SAML provider failure or network partition) preve... - **THR-012**: Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 5.7**: [OWASP] Metadata and object-level access control must be enforced; access control polici... - **[NIST] AC-3**: [NIST] Access Enforcement: The information system enforces approved authorizations for ... - **[ISO27001] A.8.1.1**: [ISO27001] Assets associated with information and information processing facilities should ... **Verification:** Test access to objects with different tag/ownership combinations and review policy evaluation logs., Review enforcement points, run policy conformance tests, and inspect authorization logs for policy application., Inspect the asset inventory and mapping of owners/classifications to access controls. **Priority:** Critical | **Status:** Pending --- #### REQ-007: Experiment tracking with artifact versioning, metric collection, structured metadata, and visualizat... **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-014**: Embeddings stored in vector DBs contain encoded PII or sensitive training data w... - **THR-015**: Attacker registers a malicious model artifact with forged metadata (faking prove... - **THR-018**: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide m... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 10.3**: [OWASP] Protect data integrity and provide mechanisms for versioning and tamper-evidence... - **[NIST] CM-3**: [NIST] Configuration Change Control: The organization develops, documents, and maintain... - **[ISO27001] A.12.5.1**: [ISO27001] Changes to information processing facilities and systems should be controlled to... **Verification:** Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications., Review change tracking records, test ability to view diffs between versions, and confirm baselines exist for experiments. **Priority:** Critical | **Status:** Pending --- #### REQ-008: Side-by-side experiment comparison UI with ability to select multiple runs, align metrics, and compa... **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-018**: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide m... - **THR-021**: Insufficient logging/auditing of prompts, LLM responses, and system decisions le... - **THR-024**: Secrets or service credentials accidentally exposed in CI/CD build logs or pipel... - *...and 4 more threats* **Security Controls:** - **[OWASP] ASVS 10.3**: [OWASP] Protect data integrity and provide mechanisms for versioning and tamper-evidence... - **[NIST] CM-3**: [NIST] Configuration Change Control: The organization develops, documents, and maintain... - **[ISO27001] A.12.5.1**: [ISO27001] Changes to information processing facilities and systems should be controlled to... **Verification:** Verify artifact signing/checksum verification, test tamper detection, and review version history logs for integrity., Review change management tickets tied to experiments and audit logs for unauthorized modifications., Review change tracking records, test ability to view diffs between versions, and confirm baselines exist for experiments. **Priority:** Critical | **Status:** Pending --- #### REQ-009: Save and share experiment configurations as reusable templates with permission controls **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-017**: Chat history export feature or saved transcripts accessible without proper autho... - **THR-026**: Insufficient tenant isolation (shared DB schema without row-level security or in... - **THR-030**: Misconfigured external ML platform connectors or cloud storage integrations expo... **Security Controls:** - **[OWASP] ASVS 5.9**: [OWASP] Ensure that sharing and template features respect authorization policies; abilit... - **[NIST] AC-6**: [NIST] Least Privilege: The organization ensures that users are granted the least privi... - **[ISO27001] A.13.2.1**: [ISO27001] Information transfer policies should include controls for sharing and distributi... **Verification:** Test sharing scenarios across roles, attempt unauthorized shares, and inspect share audit logs., Review permission assignments, simulate least privilege violations, and check enforcement logs., Review information transfer policies and check logs for adherence to procedures during sharing operations. **Priority:** High | **Status:** Pending --- #### REQ-010: Activity feed and audit logging for project events (create/update/delete), role changes, logins, dep... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 11.1**: [OWASP] Application must generate logs for security-relevant events and ensure logs cann... - **[NIST] AU-2**: [NIST] Audit Events: The information system must determine and record auditable events ... - **[ISO27001] A.12.4.1**: [ISO27001] Event logs recording user activities, exceptions, and information security event... **Verification:** Check log retention configurations, review access controls to logs, and confirm logs include required event types., Inspect logs for required events, attempt tampering to validate protections, and verify retention/rotation policies., Review audit event definitions, inspect sample logs, and validate completeness against event list. **Priority:** Critical | **Status:** Pending --- #### REQ-011: Conversational AI assistant accessible via chat UI, integrated into project context and respecting R... **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - **THR-008**: Compromised CI/CD or deployment credentials are used to deploy malicious or back... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 10.1**: [OWASP] Sensitive data processed by application features like chat should be classified ... - **[NIST] PL-8**: [NIST] Information Security Architecture: Ensure that system design includes protection... - **[ISO27001] A.18.1.4**: [ISO27001] Personal data shall be protected in accordance with relevant legislation and org... **Verification:** Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms., Review architecture diagrams, validate data flows, and confirm presence of privacy controls and logging/monitoring for LLM services. **Priority:** Critical | **Status:** Pending --- #### REQ-012: Natural language search across experiments, models, datasets and chat history with access control fi... **Related Threats:** - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-010**: Identity provider outage (OIDC/SAML provider failure or network partition) preve... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 9.3**: [OWASP] Search functionality must enforce access control checks on results and avoid inf... - **[NIST] AC-19**: [NIST] Access Control for Mobile Devices and Remote Access: Limitations and protections... - **[ISO27001] A.9.2.5**: [ISO27001] Access rights should be reviewed to ensure that search and retrieval functions d... **Verification:** Test search queries across roles/projects, attempt to infer existence of items via side-channels, and review index access controls., Review search API auth enforcement, rate-limit configurations, and audit logs for search access patterns., Check results of access reviews, and validate that changes to permissions update search index behavior. **Priority:** Critical | **Status:** Pending --- #### REQ-013: AI-powered code suggestions and explanations for ML workflows with guardrails to prevent unsafe or p... **Related Threats:** - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - **THR-008**: Compromised CI/CD or deployment credentials are used to deploy malicious or back... - **THR-010**: Identity provider outage (OIDC/SAML provider failure or network partition) preve... - *...and 3 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-014: Automated insights and recommendations derived from experiment results, with provenance metadata and... **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-014**: Embeddings stored in vector DBs contain encoded PII or sensitive training data w... - **THR-015**: Attacker registers a malicious model artifact with forged metadata (faking prove... - **THR-018**: Attacker manipulates time-series metrics (model accuracy, drift stats) to hide m... - *...and 3 more threats* **Security Controls:** - **[OWASP] ASVS 10.4**: [OWASP] AI-generated outputs that may expose sensitive information must be sanitized and... - **[NIST] SI-11**: [NIST] Error Handling and Information Leakage: Applications must detect and protect aga... - **[ISO27001] A.9.4.4**: [ISO27001] Controls should be applied to tools and utilities that can access sensitive info... **Verification:** Review logs to ensure prompt/output recording and filtering, test the system by feeding sensitive prompts and verifying redaction., Conduct tests that confirm detection of secret patterns, review human-in-the-loop approvals, and audit output logs for leakage incidents., Review access controls for AI tooling, usage logs, and approval workflows for privileged AI features. **Priority:** Critical | **Status:** Pending --- #### REQ-015: Context-aware help that understands the current project, user role and dataset context while enforci... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-002**: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m... - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 5.2**: [OWASP] Contextual features must honor authorization policies and ensure that help or gu... - **[NIST] AC-6(1)**: [NIST] Least Privilege Enforcement: Systems that provide context-based capabilities mus... - **[ISO27001] A.7.1.2**: [ISO27001] Users must be made aware of their security responsibilities; context-aware help ... **Verification:** Perform privilege escalation tests using contextual help and log analysis to ensure scoping is honored., Review help content against policy and gather user feedback on compliance alignment., Test help outputs in various roles/projects and confirm no unauthorized data is revealed. **Priority:** High | **Status:** Pending --- #### REQ-016: AI-generated experiment summaries and model cards with ability to review, edit and approve before pu... **Related Threats:** - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - **THR-008**: Compromised CI/CD or deployment credentials are used to deploy malicious or back... - **THR-012**: Cross-Site Request Forgery (CSRF) enabling unauthorized state-changing actions (... - *...and 5 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-017: Per-user chat history storage with export and deletion controls, retention policy and ability to opt... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - *...and 5 more threats* **Security Controls:** - **[OWASP] ASVS 10.6**: [OWASP] Provide mechanisms for users to export their data and request deletion; systems ... - **[NIST] MP-6**: [NIST] Media Sanitization: Ensure data is properly removed from storage when deletion/e... - **[ISO27001] A.18.1.4**: [ISO27001] Implement procedures to support privacy rights, including data access, export, a... **Verification:** Inspect sanitization procedures, attempt to recover deleted chat artifacts from backups, and verify logs of completed sanitization., Test export and deletion flows end-to-end including backups, and verify authenticity checks and propagation to dependent systems., Review data subject request procedures, test sample export-delete requests, and validate audit trail of actions. **Priority:** Critical | **Status:** Pending --- #### REQ-018: Multi-language support for the AI assistant including language detection and localization of system ... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - **THR-013**: Prompt injection attacks where user-supplied data or adversarial content manipul... - **THR-017**: Chat history export feature or saved transcripts accessible without proper autho... - **THR-021**: Insufficient logging/auditing of prompts, LLM responses, and system decisions le... - *...and 1 more threats* **Security Controls:** - **[OWASP] ASVS 10.1**: [OWASP] Sensitive data processed by application features like chat should be classified ... - **[NIST] PL-8**: [NIST] Information Security Architecture: Ensure that system design includes protection... - **[ISO27001] A.18.1.4**: [ISO27001] Personal data shall be protected in accordance with relevant legislation and org... **Verification:** Review compliance documentation, consent flows, and test export/deletion processes for chat transcripts., Review encryption configurations, test access control enforcement to chat data, and validate classification/masking mechanisms., Review architecture diagrams, validate data flows, and confirm presence of privacy controls and logging/monitoring for LLM services. **Priority:** Critical | **Status:** Pending --- #### REQ-019: Dataset upload and management via web UI and APIs with file validation, virus scanning, and optional... **Related Threats:** - **THR-001**: Phishing or credential-theft attack (including stolen password or OAuth tokens) ... - **THR-002**: Forged or replayed SSO/OIDC/SAML assertions or JWTs accepted by gateway due to m... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-013**: Prompt injection attacks where user-supplied data or adversarial content manipul... - *...and 5 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- #### REQ-020: Data preview for tabular, image and structured data in browser with row/column-level access controls... **Related Threats:** - **THR-003**: Cross-Site Scripting (XSS) or supply-side injection in the SPA allowing executio... - **THR-004**: SQL injection or other injection vulnerabilities in APIs or query builders allow... - **THR-005**: WebSocket/real-time channels leaking sensitive project comments, chat content or... - **THR-006**: Publicly exposed object storage (e.g., S3 bucket misconfiguration) or overly per... - **THR-007**: Sensitive context (PII, secret keys, model IP) included in prompts or context wi... - *...and 5 more threats* **Verification:** Manual Review **Priority:** Medium | **Status:** Pending --- *Showing detailed mappings for 20 of 35 requirements.* --- ## Appendix E: References - [OWASP ASVS 5.0](https://owasp.org/www-project-application-security-verification-standard/) - [NIST Cybersecurity Framework](https://www.nist.gov/cyberframework) - [ISO/IEC 27001:2022](https://www.iso.org/standard/27001) - [OWASP Top 10](https://owasp.org/www-project-top-ten/) - [MITRE ATT&CK Framework](https://attack.mitre.org/) --- *End of Report - Generated by Security Requirements Analysis System v2.0* *Generated: 2025-11-19 16:47:15*